Object Viewpoint Research Articles

A simple optical flow model explains why certain object viewpoints are special.

A core challenge in perception is recognizing objects across the highly variable retinal input that occurs when objects are viewed from different directions (e.g. front versus side views). It has long been known that certain views are of particular importance, but it remains unclear why. We reasoned that characterizing the computations underlying visual comparisons between objects could explain the privileged status of certain qualitatively special views. We measured pose discrimination for a wide range of objects, finding large variations in performance depending on the object and the viewing angle, with front and back views yielding particularly good discrimination. Strikingly, a simple and biologically plausible computational model based on measuring the projected three-dimensional optical flow between views of objects accurately predicted both successes and failures of discrimination performance. This provides a computational account of why certain views have a privileged status.

Spatial and temporal consistency learning for monocular 6D pose estimation

Monocular 6D pose estimation is a challenging task in the field of computer vision and robotics. Many previous works only input the cropped image of single object during training and inference, aiming to remove the noise from non-object regions. However, most of these methods ignore the viewpoint and spatial relationships of objects in the scene, which are crucial for accurate pose estimation of camera. To address this issue, this paper proposes a novel multi-view and multi-object based learning strategy for monocular 6D pose estimation, which involves the consistency of object coordinate for the same object at different viewpoints and the consistency of world coordinate for different objects in the same space. In the proposed method, the spatial and temporal groups are generated to trained the monocular 6D pose estimation network. Due to the camera motion, scene images taken at different times can be regarded as images captured from different viewpoints. Therefore, a temporal consistency loss is designed to constraint the relationship of the same object at different viewpoints, while a spatial consistency loss is designed to constraint the relationship of different objects at the same space. Finally, the proposed method is verified on the public datasets. Experimental results show that the proposed method is accurate, robust, and outperforms similar state-of-the-art approaches.

Mental object rotation based on two-dimensional visual representations

The discovery of mental rotation was one of the most significant landmarks in experimental psychology, leading to the ongoing assumption that to visually compare objects from different three-dimensional viewpoints, we use explicit internal simulations of object rotations, to 'mentally adjust' one object until it matches the other1. These rotations are thought to be performed on three-dimensional representations of the object, by literal analogy to physical rotations. In particular, it is thought that an imagined object is continuously adjusted at a constant three-dimensional angular rotation rate from its initial orientation to the final orientation through all intervening viewpoints2. While qualitative theories have tried to account for this phenomenon3, to date there has been no explicit, image-computable model of the underlying processes. As a result, there is no quantitative account of why some object viewpoints appear more similar to one another than others when the three-dimensional angular difference between them is the same4,5. We reasoned that the specific pattern of non-uniformities in the perception of viewpoints can reveal the visual computations underlying mental rotation. We therefore compared human viewpoint perception with a model based on the kind of two-dimensional 'optical flow' computations that are thought to underlie motion perception in biological vision6, finding that the model reproduces the specific errors that participants make. This suggests that mental rotation involves simulating the two-dimensional retinal image change that would occur when rotating objects. When we compare objects, we do not do so in a distal three-dimensional representation as previously assumed, but by measuring how much the proximal stimulus would change if we watched the object rotate, capturing perspectival appearance changes7.

On the robustness of self-supervised representations for multi-view object classification

• Analyse self-supervised and supervised embeddings regarding viewpoint robustness in both controlled and real-world settings. • Controlled setting created by approximating view using a homography on multiple common benchmark datasets. • Performance in-the-wild quantified by analysing the representations on multiple real-world, multi-view datasets. • Demonstrate that self-supervised representations are more robust to both controlled and real-world settings. • Show that self-supervised representations require less context than supervised ones in recognising objects in novel views. It is known that representations from self-supervised pre-training can perform on par, and often better, on various downstream tasks than representations from fully-supervised pre-training. This has been shown in a host of settings such as generic object classification and detection, semantic segmentation, and image retrieval . However, some issues have recently come to the fore that demonstrate some of the failure modes of self-supervised representations, such as performance on non-ImageNet-like data, or complex scenes. In this paper, we show that self-supervised representations based on the instance discrimination objective lead to better representations of objects that are more robust to changes in the viewpoint and perspective of the object. We perform experiments of modern self-supervised methods against multiple supervised baselines to demonstrate this, including approximating object viewpoint variation through homographies , and real-world tests based on several multi-view datasets. We find that self-supervised representations are more robust to object viewpoint and appear to encode more pertinent information about objects that facilitate the recognition of objects from novel views.

Grazing, non-transiting disintegrating exoplanets observed with the planned Ariel space observatory

Disintegrating/evaporating rocky exoplanets can be observed not only as transiting planets, but also in a grazing, non-transiting regime, where the solid body of the planet does not transit, but part of the comet-like tail can transit. In this case the forward scattering on the escaping particles is the dominant process, which amplifies the photometric signal of the parent star detected by the observer. The change in the flux is small, about 10^-3 (1000 ppm) at the best properties of the planetary system, but if the observation is enough precise, the detection is possible. The planned Ariel space observatory is designed to achieve a stability of < 100 ppm (the goal is 10 ppm) over the temporal bandwidth of the transit, typically less than 10 hours. In this case study we took the disintegrating exoplanet Kepler-1520b and changed the orbital properties of the system to get a grazing, non-transiting orbit scenario, and investigated, how different particle sizes, species, Ariel observational channels, and other factors affect the amplitude of the forward-scattering peak, and the detectability of the scattering event. Our most important result is that the forward-scattering amplitude is not sensitive to the dust composition, but is very sensitive to the particle size, observational channel, and other factors. These factors can reduce mainly the detectability of 1-micron grains. 0.1-micron grains will be detectable at short wavelengths. 0.01-micron grains generate long and very small forward scattering amplitude, which is below the detection limit. Based on our results we can conclude that using Ariel will be possible to detect and investigate not only transiting, but also grazing, non-transiting disintegrating exoplanets based on the forward scattering. From the viewpoint of such objects the big advantage of Ariel will be the possibility of multiwavelength observations.

Extreme heat events from an object viewpoint with application to south‐east Australia

AbstractSurface high temperature extremes have a large impact on society and environment, and their relationship to the large‐scale circulation has been explored in detail. However, most of these studies have investigated these relationships in a single location or region, and focused on events that persist at this location sufficiently long to be considered heatwave‐like events. Moreover, few studies have explored the full lifecycle of extreme temperature events. The present study introduces a simple object that can be used to follow areas of extreme temperature that moves through time, and presents a basic global climatology of these objects, showing that shorter high temperature objects are more frequently found in the tropics and longer objects are more likely along the mid‐latitude jet regions. Heatwave‐length (three or more days) high temperature events in the south‐eastern part of Australia are also explored using this object approach, showing that while many shorter length objects in the area form and decay within the region, longer objects tend to first appear along the coast west of much of the south‐east Australian region and decay mostly over the ocean to the south‐east of the region. The area affected by the objects is closely associated to the propagation of circulation anomalies, moving from west to east with surface temperature extremes slightly to the west of upper‐level anticyclonic anomalies and in between a surface high to the east and a trough/low/cold front to the west. The large‐scale circulation anomalies are similar in structure but differ in magnitude for different lengths of objects that affect south‐east Australia, although longer‐lived objects have upper‐level anticyclonic anomalies located further north than the shorter heatwave‐length objects. Objects that affect both far western and south‐eastern Australia closely resemble the longer south‐eastern Australian heatwave‐length objects.

Object-Wise Video Editing

Beyond time frame editing in video data, object level video editing is a challenging task; such as object removal in a video or viewpoint changes. These tasks involve dynamic object segmentation, novel view video synthesis and background inpainting. Background inpainting is a task of the reconstruction of unseen regions presented by object removal or viewpoint change. In this paper, we propose a video editing method including foreground object removal background inpainting and novel view video synthesis under challenging conditions such as complex visual pattern, occlusion, overlaid clutter and variation of depth in a moving camera. Our proposed method calculates a weighted confidence score on the basis of normalized difference between observed depth and predicted distance in 3D space. A set of potential points from epipolar lines from neighbor frames are collected, refined, and weighted to select a few number of highly qualified observations to fill the desired region of interest area in the current frame from video. Based on the background inpainting method, novel view video synthesis is conducted with arbitrary viewpoint. Our method is evaluated with both a public dataset and our own video clips and compared with multiple state of the art methods showing a superior performance.

Image-Text Multimodal Emotion Classification via Multi-View Attentional Network

Compared with single-modal content, multimodal data can express users’ feelings and sentiments more vividly and interestingly. Therefore, multimodal sentiment analysis has become a popular research topic. However, most existing methods either learn modal sentiment feature independently, without considering their correlations, or they simply integrate multimodal features. In addition, most publicly available multimodal datasets are labeled by sentiment polarities, while the emotions expressed by users are specific. Based on this observation, in this paper, we build a large-scale image-text emotion dataset (i.e., labeled by different emotions), called TumEmo, with more than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">190,000</i> instances from Tumblr. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We further propose a novel multimodal emotion analysis model based on the Multi-view Attentional Network (MVAN), which utilizes a memory network that is continually updated to obtain the deep semantic features of image-text. The model includes three stages: feature mapping, interactive learning, and feature fusion. In the feature mapping stage, we leverage image features from an object viewpoint and a scene viewpoint to capture effective information for multimodal emotion analysis. Then, an interactive learning mechanism is adopted that uses the memory network; this mechanism extracts single-modal emotion features and interactively models the cross-view dependencies between the image and text. In the feature fusion stage, multiple features are deeply fused using a multilayer perceptron and a stacking-pooling module. The experimental results on the MVSA-Single, MVSA-Multiple, and TumEmo datasets show that the proposed MVAN outperforms strong baseline models by large margins.

Size-invariant but location-specific object-viewpoint adaptation in the absence of awareness

Perceiving object viewpoint is important for appropriate action. Here we investigated whether viewpoint information could be represented in the absence of awareness, by measuring viewpoint adaptation aftereffect from visual objects rendered invisible through interocular suppression. Participants adapted to either a visible or an invisible line-drawing cube with unambiguous viewpoint, then viewed an ambiguous Necker cube and reported its perceived viewpoint. In both the visible and invisible adaptation conditions, participants more likely perceived the Necker cube in opposite viewpoint compared to the adapting cube. Interestingly, this viewpoint aftereffect was still observed when the adapting and testing cubes were different in size. However, when the testing Necker cube was in a different location, the viewpoint aftereffect was only observed following visible adapting cube, abolished when the adapting cube was invisible. Thus object viewpoint representation could be established without awareness, and such unconscious viewpoint representation is size-invariant but location-specific. Object viewpoint representation requires conscious awareness to be globally accessible.

Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features.

Searching for items that are useful given current goals, or "target" recognition, requires observers to flexibly attend to certain object properties at the expense of others. This could involve focusing on the identity of an object while ignoring identity-preserving transformations such as changes in viewpoint or focusing on its current viewpoint while ignoring its identity. To effectively filter out variation due to the irrelevant dimension, performing either type of task is likely to require high-level, abstract search templates. Past work has found target recognition signals in areas of ventral visual cortex and in subregions of parietal and frontal cortex. However, target status in these tasks is typically associated with the identity of an object, rather than identity-orthogonal properties such as object viewpoint. In this study, we used a task that required subjects to identify novel object stimuli as targets according to either identity or viewpoint, each of which was not predictable from low-level properties such as shape. We performed functional MRI in human subjects of both sexes and measured the strength of target-match signals in areas of visual, parietal, and frontal cortex. Our multivariate analyses suggest that the multiple-demand (MD) network, including subregions of parietal and frontal cortex, encodes information about an object's status as a target in the relevant dimension only, across changes in the irrelevant dimension. Furthermore, there was more target-related information in MD regions on correct compared with incorrect trials, suggesting a strong link between MD target signals and behavior. NEW & NOTEWORTHY Real-world target detection tasks, such as searching for a car in a crowded parking lot, require both flexibility and abstraction. We investigated the neural basis of these abilities using a task that required invariant representations of either object identity or viewpoint. Multivariate decoding analyses of our whole brain functional MRI data reveal that invariant target representations are most pronounced in frontal and parietal regions, and the strength of these representations is associated with behavioral performance.

A New Geometric Approach to Explain the Features of the Micro World

A novel geometric approach is proposed for the development of the wave-particle notions. This approach is based on a comparison of the two geometries with different sizes of an infinitesimal point. It is assumed that the smaller is object mass, the larger is the size of the infinitesimal point in comparison with the point of the geometry of macro world. Within this approach, the smaller is object mass, the larger is the uncertainty of its position from the viewpoint of macro objects (macro geometry). This approach provides a natural ex-planation of Heisenberg’s indeterminancy principle. Formally, this approach appears as an unusual operation with an infinitesimal value (point). However, it should be noted that unusual operations (though with infinitely large values) are already known in physics. These are unattainability of the absolute zero of temperature and unattainability of the maximal velocity of movement. Interconnection of the two geometries with different sizes of infinitesimal values is possible with the help of the direct and inverse Weierstrass transformation. At present, diffraction effects are described using the wave notions about the light and Fourier transform. The diffraction of light is usually registered at a distance not less than 1 - 3 metres between the screens in one of which there is a slit or several slits. This distance is about 106 times longer than the wavelength of the radiation. In the present work, an approach is proposed that allows one to describe the light fluxes at short distances between the screens with the help of Fourier and Weierstrass transforms.

Viewpoint refinement and estimation with adapted synthetic data

Estimating the viewpoint of objects in images is an important task for scene understanding. The viewpoint estimation accuracy, however, depends highly on the amount of training data and the quality of the annotation. While humans excel at labelling images with coarse viewpoint annotations like front, back, left or right, the process becomes tedious and the quality of the annotations decreases when finer viewpoint discretisations are required. To solve this problem, we propose a refinement of coarse viewpoint annotations, which are provided by humans, with synthetic data automatically generated from 3D models. To compensate between the difference between synthetic and real images, we introduce a domain adaptation approach that aligns the domain of the synthesized images with the domain of the real images. Experiments show that the proposed approach significantly improves viewpoint estimation on several state-of-the-art datasets.

Semantic SLAM for an AUV using object recognition from point clouds

This paper presents a navigation and mapping system for an autonomous underwater vehicle (AUV) while operating near a man-made underwater environment. The objective is to recognize objects (or object parts) and use these as landmarks for simultaneous localization and mapping (SLAM). This approach is intended as the first step towards autonomous object manipulation, to be carried out at a later stage. The approach contains two main components: Object recognition from range data, and feature-based semantic SLAM. For the first component we propose an automatic method for the recognition and location of 3D objects using 3D point clouds as input, extracted from a laser scanner. Since it is common in inspection maintenance and repair (IMR) applications to have access to the 3D models of the objects of interest, the proposed method assumes a priori knowledge of the 3D models of these objects. In typical man-made environments, such objects can be distinct components of a structure, such as valves, pipes and wet-mateable connectors. The object recognition is based on recently proposed global descriptors for point clouds, that allow a compact description of the object shape, which is independent of the object view point. Once an object is recognized, its pose with respect to the AUV is determined using an ICP-based method. The second component of the approach is a feature based SLAM algorithm that uses the recognized objects as landmarks to improve the AUV navigation. The paper presents preliminary results obtained with the Girona 500 AUV, equipped with a fast laser scanner recently developed at the University of Girona. Tests conducted in a controlled environment (water tank) illustrate the suitability of the approach.

Context-based object viewpoint estimation: A 2D relational approach

The task of object viewpoint estimation has been a challenge since the early days of computer vision. To estimate the viewpoint (or pose) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, extrinsic elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been proven to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for viewpoint estimation. In particular, we look at object configurations by following a relational neighbor-based approach for reasoning about object relations. We show that, starting from noisy object detections and viewpoint estimates, exploiting the estimated viewpoint and location of other objects in the scene can lead to improved object viewpoint predictions. Experiments on the KITTI dataset demonstrate that object configurations can indeed be used as a complementary cue to appearance-based viewpoint estimation. Our analysis reveals that the proposed context-based method can improve object viewpoint estimation by reducing specific types of viewpoint estimation errors commonly made by methods that only consider local information. Moreover, considering contextual information produces superior performance in scenes where a high number of object instances occur. Finally, our results suggest that, following a cautious relational neighbor formulation brings improvements over its aggressive counterpart for the task of object viewpoint estimation.

Archiving cultural and community memories in a networked information society : a Japanese perspective

INTRODUCTIONMany projects to develop digital collections of cultural heritage have been carried out worldwide since the explosive growth of the Internet and the Web in the mid-1990s. The author has discussed digital archives and metadata in the context of community memory and cultural resources in previous articles (Sugimoto, 2014; Sugimoto, Mitsuharu, Tetsuya, &Tsunagu, 2015). This paper looks back at specifically Japanese projects involving cultural heritage and community memories and discusses some issues from the author's perspectivewith the intention to provide an overview of Japanese digital library activities more comprehensively.Large digital library projects were carried out in the 1990s. For example, American Memory and Making of America by large research libraries, and the Digital Library Initiatives by the U.S. National Science Foundation and other funding bodies are the most famous projects. American Memory and Making of America developed large collections of historical resources. The Digital Library Initiatives have funded many research projects of different types-information and communication technology (ICT) centric projects, collection-centric projects, and user-centric projects. In Japan, several major digitization projects were started by the National Diet Library (NDL) and major memory institutions. IPA/JIPDEC conducted a comprehensive digital library project in collaboration with the National Diet Library (Mukaiyama, 2000). The National Diet Library started the Kindai Digital Library, a huge digital collection of books published in Japan after the revolution in the 19th century. The Japan Center for Asian Historical Records at the National Archives of Japan started building a digital collection of Japanese governmental records from the revolution in the 19th century to the end of World War II. The museum community has also had major works for building digital collections. Cultural Heritage Online is a collaborative project by the nation-wide museum community under the Agency for Cultural Affairs of the Japanese government.In Japan, digital archive is commonly used as a term meaning a large collection of digital resources, primarily in the cultural domain. Various types of digital collections have been built as a digital archive-digitized books, digital images of museum resources, 3D images of heritage sites, etc. Libraries, museums, archives and related institutions, referred to as memory institutions, are the key players in building digital archives.There are also digital archives for non-cultural resources. Building digital archives of disaster records is a crucial activity as it not only keeps records of disasters, but also keeps community memories for the future. Many digital archives were created after the Great East Japan Earthquake and Tsunami on March 11, 2011. Many digital photographs and videos were taken at the disaster by people who witnessed the disaster, and many photographs and other data were created after the disaster to record the disaster itself and recovery from it. There are many non-digital materials which should be archived in addition to born digital resources. Thus, these archives collect both born digital materials as well as digitized materials. The National Diet Library is providing a portal for the Great East Japan Earthquake and Tsunami archives, which is named Hinagiku.Digital archives refer to a collection of digital objects of cultural content which may be born digital or converted into digital, for example photographs, videos, audio records and documents. On the other hand, many databases of cultural resources that have been or are being built provide only metadata of the cultural resources with no primary digital objects. These are not strictly digital archives from the viewpoint of primary digital objects, but this paper also mentions them as the border between digital archives and databases is not crisp, and metadata about cultural resources are a key component of digital archives. …

Influence of Two Cameras Positioning to 3D Reconstruction

For last few years plenty of attention is dedicated to 3D object recognition and 3D reconstruction. Not a few produced robots perceive ambience with assistance of two cameras, analyzing perceived views. In this paper we analyze two camera positioning task. We change the angle between two cameras (principal axes) and analyze the influence of camera positioning to first steps of 3D reconstruction algorithm. One of the ways of camera mounting is in parallel, when all perceived points lie on the same plane. Unfortunately, because of influence of internal and external factors we receive different errors in 3D reconstruction. The influence of these errors can be reduced by minimizing object distance to camera and changing the angle between two cameras. Changing the angle between two cameras we change the object viewpoint. In our approach we show that even if object makes fairly wide angle with camera plane (60°–80°), we can estimate to 20 % of suitable feature points with selected Harris and Stephens corner detector. Ill. 5, bibl. 11 (in Lithuanian; summaries in Lithuanian, English, Russian).

Size precedes view: developmental emergence of invariant object representations in lateral occipital complex.

Although object perception involves encoding a wide variety of object properties (e.g., size, color, viewpoint), some properties are irrelevant for identifying the object. The key to successful object recognition is having an internal representation of the object identity that is insensitive to these properties while accurately representing important diagnostic features. Behavioral evidence indicates that the formation of these kinds of invariant object representations takes many years to develop. However, little research has investigated the developmental emergence of invariant object representations in the ventral visual processing stream, particularly in the lateral occipital complex (LOC) that is implicated in object processing in adults. Here, we used an fMR adaptation paradigm to evaluate age-related changes in the neural representation of objects within LOC across variations in size and viewpoint from childhood through early adulthood. We found a dissociation between the neural encoding of object size and object viewpoint within LOC: by age of 5-10 years, area LOC demonstrates adaptation across changes in size, but not viewpoint, suggesting that LOC responses are invariant to size variations, but that adaptation across changes in view is observed in LOC much later in development. Furthermore, activation in LOC was correlated with behavioral indicators of view invariance across the entire sample, such that greater adaptation was correlated with better recognition of objects across changes in viewpoint. We did not observe similar developmental differences within early visual cortex. These results indicate that LOC acquires the capacity to compute invariance specific to different sources of information at different time points over the course of development.

Moment invariants to affine transformation of colours

Most colour descriptors are not robust because they are constructed for simple colour transformations, such as a diagonal-offset transformation. In this paper, a type of colour descriptor is proposed which is composed of rational expression of moments that are invariant to full affine photometric transformations of the 3 colour bands, called a colour affine moment invariant (CAMI). The construction method, based on multiple integrations of colour cores, is intuitive, and it is generic because it can recover existing CAMIs and generate infinitely many new CAMIs as well. Through comprehensive experiments, the effectiveness of one set of newly generated CAMIs is validated. It is shown that CAMIs can better cope with changing illuminations and white balance changes than some typical colour descriptors and are moderately robust against small changes in viewpoint of 3D objects.

Repetition Blindness for Natural Images of Objects with Viewpoint Changes

When stimuli are repeated in a rapid serial visual presentation (RSVP), observers sometimes fail to report the second occurrence of a target. This phenomenon is referred to as “repetition blindness” (RB). We report an RSVP experiment with photographs in which we manipulated object viewpoints between the first and second occurrences of a target (0°, 45°, or 90° changes), and spatial frequency (SF) content. Natural images were spatially filtered to produce low, medium, or high SF stimuli. RB was observed for all filtering conditions. Surprisingly, for full-spectrum (FS) images, RB increased significantly as the viewpoint reached 90°. For filtered images, a similar pattern of results was found for all conditions except for medium SF stimuli. These findings suggest that object recognition in RSVP are subtended by viewpoint-specific representations for all spatial frequencies except medium ones.

A 52 mW Full HD 160-Degree Object Viewpoint Recognition SoC With Visual Vocabulary Processor for Wearable Vision Applications

A 1920 × 1080 160° object viewpoint recognition system-on-chip (SoC) is presented in this paper. The SoC design is dedicated to wearable vision applications, and we address several crucial issues including the low recognition accuracy due to the use of low resolution images and dramatic changes in object viewpoints, and the high power consumption caused by the complex computations in existing computer vision object recognition systems. The human-centered design (HCD) mechanism is proposed in order to maintain a high recognition rate in difficult situations. To overcome the degradation of accuracy when dramatic changes to the object viewpoint occur, the object viewpoint prediction (OVP) engine in the HCD provides 160° object viewpoint in- variance by synthesizing various object poses from predicted object viewpoints. To achieve low power consumption, the visual vocabulary processor (VVP), which is based on bag-of-words (BoW) matching algorithm, is used to advance the matching stage from the feature-level to the object-level and results in a 97% reduction in the required memory bandwidth compared to previous recognition systems. Moreover, the matching efficiency of the VVP enables the system to support real-time full HD (1920 × 1080) processing, thereby improving the recognition rate for detecting a traffic light at a distance of 50 m to 95% compared to the 29% recognition rate for VGA (640 × 480) processing. The real-time 1920 × 1080 visual recognition chip is realized on a 6.38 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> die with 65 nm CMOS technology. It achieves an average recognition rate of 94%, a power efficiency of 1.18 TOPS/W, and an area efficiency of 25.9 GOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> while only dissipating 52 mW at 1.0 V.