Natural Image Sequences Research Articles

Spatial Feature-Based ISAR Image Registration for Space Targets

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.

A neural network model for online one-shot storage of pattern sequences.

Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as 'replay' or 'offline-learning', which helps in improving the associations and consolidating the learned patterns.

Poster Session: Perceived image quality of natural images through different bifocal corrections after adaptation to sharp or blur.

Simultaneous Vision, present in bifocal and multifocal contact or intraocular lenses, is a common clinical strategy for presbyopia. Neural adaptation is known to occur, but it is not predictable nor well understood. Previous works have studied the short-term neural adaptation to bifocal images using Adaptive Optics and digital convolutions of a face image (with pure defocus and simultaneous images) as adaptation images, and also as test images for perceptual judgments (Radhakrishnan et al., 2014). In this study, we use a programmable see-through head-mounted visual simulator (SimVis Gekko) to analyze the effect of neural adaptation on the perceived image quality of different bifocal corrections (BC) with different balances of energy between far and near, and for different transitions between adaptation and test. Eight subjects judged a video sequence of urban natural images, through two monofocal corrections (0 and 3D) and nine BCs (far/near balance from 90/10 to 10/90 and an addition of 3.00D), optically simulated monocularly. Two initial adaptation states of 10s were used: 0.00D-SHARP and 3.00D-BLUR, followed a transition from these states to the BC that can be ABRUPT (with 1, 5 or 30s of adaptation), or SMOOTH (linear transition lasting 1, 5 or 30s). Our results show a significant increase in perceptual quality after adaptation to BLUR, particularly for intermediate energy balances. However, subjects are quite insensitive to the type and duration of the transition.

Adaptation supports short-term memory in a visual change detection task.

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.

The roles of gaze and head orientation in face categorization during rapid serial visual presentation

Little is known about how perceived gaze direction and head orientation may influence human categorization of visual stimuli as faces. To address this question, a sequence of unsegmented natural images, each containing a random face or a non-face object, was presented in rapid succession (stimulus duration: 91.7 ms per image) during which human observers were instructed to respond immediately to every face presentation. Faces differed in gaze and head orientation in 7 combinations – full-front views with perceived gaze (1) directed to the observer, (2) averted to the left, or (3) averted to the right, left ¾ side views with (4) direct gaze or (5) averted gaze, and right ¾ side views with (6) direct gaze or (7) averted gaze – were presented randomly throughout the sequence. We found highly accurate and rapid behavioural responses to all kinds of faces. Crucially, both perceived gaze direction and head orientation had comparable, non-interactive effects on response times, where direct gaze was responded faster than averted gaze by 48 ms and full-front view faster than ¾ side view also by 48 ms on average. Presentations of full-front faces with direct gaze led to an additive speed advantage of 96 ms to ¾ faces with averted gaze. The results reveal that the effects of perceived gaze direction and head orientation on the speed of face categorization probably depend on the degree of social relevance of the face to the viewer.

Parallel Adaptation to Spatially Distinct Distortions.

Optical distortions as a visual disturbance are inherent in many optical devices such as spectacles or virtual reality headsets. In such devices, distortions vary spatially across the visual field. In progressive addition lenses, for example, the left and right regions of the lens skew the peripheral parts of the wearers visual field in opposing directions. The human visual system adapts to homogeneous distortions and the respective aftereffects are transferred to non-retinotopic locations. This study investigates simultaneous adaptation to two opposing distortions at different retinotopic locations. Two oppositely skewed natural image sequences were presented to 10 subjects as adaptation stimuli at two distinct locations in the visual field. To do so, subjects were instructed to keep fixation on a target. Eye tracking was used for gaze control. Change of perceived motion direction was measured in a direction identification task. The point of subjective equality (PSE), that is, the angle at which a group of coherently moving dots was perceived as moving horizontal, was determined for both retinal locations. The shift of perceived motion direction was evaluated by comparing PSE before and after adaptation. A significant shift at both retinal locations in the direction of the skew distortion of the corresponding adaptation stimulus is demonstrated. Consequently, parallel adaptation to two opposing distortions in a retinotopic reference frame was confirmed by this study.

Multi‐exposure image fusion via a pyramidal integration of the phase congruency of input images with the intensity‐based maps

The most important part of the common algorithms for multi-exposure image fusion (MEF) is the selection of features and metrics that are appropriate for weight map extraction. This study presents a structure-based multi-exposure image fusion by employing the phase congruency (PC) of the input image. The main idea behind PC-based analysis is that the locations of image key attributes are at points where frequency components are maximally in phase. PC detects the details of an image invariant to its contrast and also emphasises on the texture- or structure-based features. In this work, alongside intensity-based maps, the extracted PC-based map is utilised for MEF in a pyramidal manner. Several experiments conducted on the benchmark dataset including a variety of natural multi-exposed image sequences to evaluate the proposed algorithm. Quantitative evaluations in terms of MEF structural similarity index and visual quality assessments show that the proposed method achieves better performance and produces comparable fused images in comparison to other approaches.

A robust neural familiar face recognition response in a dynamic (periodic) stream of unfamiliar faces

We present an objective and sensitive approach to measure human familiar face recognition (FFR) across variable facial identities. Twenty-six participants viewed sequences of natural images of different unfamiliar faces presented at a fixed rate of 6 Hz (i.e., 6 faces by second), with variable natural images of different famous face identities appearing periodically every 7th image (i.e., .86 Hz). Participants were unaware of the goal of the study and performed an orthogonal task. Following only seven minutes of visual stimulation, the FFR response was objectively identified in the EEG spectrum at .86 Hz and its harmonics (1.71 Hz, etc.) over bilateral occipito-temporal regions, being significant in every individual participant. When the exact same images appeared upside-down, the FFR response amplitude reduced by more than 80%, and was uncorrelated across individuals to the upright face response. The FFR for upright faces emerges between 160 and 200 msec following the famous face onset over bilateral occipito-temporal region and lasts until about 560 msec. The stimulation paradigm offers an unprecedented way to characterize rapid and automatic human face familiarity recognition across individuals, during development and clinical conditions, also providing original information about the time-course and neural basis of human FFR in temporally constrained stimulation conditions with natural images.

Without low spatial frequencies, high resolution vision would be detrimental to motion perception.

A normally sighted person can see a grating of 30 cycles per degree or higher, but spatial frequencies needed for motion perception are much lower than that. It is unknown for natural images with a wide spectrum how all the visible spatial frequencies contribute to motion speed perception. In this work, we studied the effect of spatial frequency content on motion speed estimation for sequences of natural and stochastic pixel images by simulating different visual conditions, including normal vision, low vision (low-pass filtering), and complementary vision (high-pass filtering at the same cutoff frequencies of the corresponding low-vision conditions) conditions. Speed was computed using a biological motion energy-based computational model. In natural sequences, there was no difference in speed estimation error between normal vision and low vision conditions, but it was significantly higher for complementary vision conditions (containing only high-frequency components) at higher speeds. In stochastic sequences that had a flat frequency distribution, the error in normal vision condition was significantly larger compared with low vision conditions at high speeds. On the contrary, such a detrimental effect on speed estimation accuracy was not found for low spatial frequencies. The simulation results were consistent with the motion direction detection task performed by human observers viewing stochastic sequences. Together, these results (i) reiterate the importance of low frequencies in motion perception, and (ii) indicate that high frequencies may be detrimental for speed estimation when low frequency content is weak or not present.

Robust online video super-resolution using an efficient alternating projections scheme

Video super-resolution reconstruction (SRR) algorithms attempt to reconstruct high-resolution (HR) video sequences from low-resolution observations. Although recent progress in video SRR has significantly improved the quality of the reconstructed HR sequences, it remains challenging to design SRR algorithms that achieve good quality and robustness at a small computational complexity, being thus suitable for online applications. In this paper, we propose a new adaptive video SRR algorithm that achieves state-of-the-art performance at a very small computational cost. Using a nonlinear cost function constructed considering characteristics of typical innovation outliers in natural image sequences and an edge-preserving regularization strategy, we achieve state-of-the-art reconstructed image quality and robustness. This cost function is optimized using a specific alternating projections strategy over non-convex sets that is able to converge in a very few iterations. An accurate and very efficient approximation for the projection operations is also obtained using tools from multidimensional multirate signal processing. This solves the slow convergence issue of stochastic gradient-based methods while keeping a small computational complexity. Simulation results with both synthetic and real image sequences show that the performance of the proposed algorithm is similar or better than state-of-the-art SRR algorithms, while requiring only a small fraction of their computational cost.

Category-selective response to periodic face stimulations in natural-image sequence degrades nonlinearly with face omissions

Category-selective response to periodic face stimulations in natural-image sequence degrades nonlinearly with face omissions

Biologically-inspired image processing in computational retina models

Retinal Prosthesis (RP) is an approach to restore vision, using an implanted device to electrically stimulate the retina. A fundamental problem in RP is to translate the visual scene to retina neural spike patterns, mimicking the computations normally done by retina neural circuits. Towards the perspective of improved RP interventions, we propose a Computer Vision (CV) image preprocessing method based on Retinal Ganglion Cells functions and then use the method to reproduce retina output with a standard Generalized Integrate & Fire (GIF) neuron model. “Virtual Retina” simulation software is used to provide the stimulus-retina response data to train and test our model. We use a sequence of natural images as model input and show that models using the proposed CV image preprocessing outperform models using raw image intensity (interspike-interval distance 0.17 vs 0.27). This result is aligned with our hypothesis that raw image intensity is an improper image representation for Retinal Ganglion Cells response prediction.

The contribution of color information to rapid face categorization in natural scenes.

Color's contribution to rapid categorization of natural images is debated. We examine its effect on high-level face categorization responses using fast periodic visual stimulation (Rossion et al., 2015). A high-density electroencephalogram (EEG) was recorded during presentation of sequences of natural object images every 83 ms (i.e., at F = 12.0 Hz). Natural face images were embedded in the sequence at a fixed interval of F/9 (1.33 Hz). There were four conditions: (a) full-color images; (b) grayscale images; and (c) and (d) phase-scrambled images from Conditions 1 and 2, respectively, making faces and objects unrecognizable. Observers' task was to respond to color changes of the fixation cross (Experiment 1). We found face-categorization responses at 1.33 Hz and its harmonics (2.67 Hz, etc.) over occipitotemporal areas, with right-hemisphere dominance; responses to color images were not significantly different from those to grayscale images. Behavioral analysis revealed longer response times when images contained color, despite nearly-all-correct performance in all conditions, suggesting that color change in the task might detract from color's contribution to face categorization. We subsequently changed the task to responding to fixation shape changes so that such response-time differences were eliminated (Experiment 2). The aggregate face-categorization response became 21.6% stronger to color than to grayscale images. This color advantage occurred late, at 290-415 ms after stimulus onset. Our results suggest that the color advantage for face categorization interacts with behavior, and that color only has a moderate and relatively late contribution to rapid face categorization in natural images.

Memory for Repeated Images in Rapid-Serial-Visual-Presentation Streams of Thousands of Images

Human observers readily detect targets in stimuli presented briefly and in rapid succession. Here, we show that even without predefined targets, humans can spot repetitions in streams of thousands of images. We presented sequences of natural images reoccurring a number of times interleaved with either one or two distractors, and we asked participants to detect the repetitions and to identify the repeated images after a delay that could last for minutes. Performance improved with the number of repeated-image presentations up to a ceiling around seven repetitions and was above chance even after only two to three presentations. The task was easiest for slow streams; performance dropped with increasing image-presentation rate but stabilized above 15 Hz and remained well above chance even at 120 Hz. To summarize, we reveal that the human brain has an impressive capacity to detect repetitions in rapid-serial-visual-presentation streams and to remember repeated images over a time course of minutes.

The Role of Bottom-Up and Top-Down Cortical Interactions in Adaptation to Natural Scene Statistics.

Adaptation is a mechanism by which cortical neurons adjust their responses according to recently viewed stimuli. Visual information is processed in a circuit formed by feedforward (FF) and feedback (FB) synaptic connections of neurons in different cortical layers. Here, the functional role of FF-FB streams and their synaptic dynamics in adaptation to natural stimuli is assessed in psychophysics and neural model. We propose a cortical model which predicts psychophysically observed motion adaptation aftereffects (MAE) after exposure to geometrically distorted natural image sequences. The model comprises direction selective neurons in V1 and MT connected by recurrent FF and FB dynamic synapses. Psychophysically plausible model MAEs were obtained from synaptic changes within neurons tuned to salient direction signals of the broadband natural input. It is conceived that, motion disambiguation by FF-FB interactions is critical to encode this salient information. Moreover, only FF-FB dynamic synapses operating at distinct rates predicted psychophysical MAEs at different adaptation time-scales which could not be accounted for by single rate dynamic synapses in either of the streams. Recurrent FF-FB pathways thereby play a role during adaptation in a natural environment, specifically in inducing multilevel cortical plasticity to salient information and in mediating adaptation at different time-scales.

Adaptation to Skew Distortions of Natural Scenes and Retinal Specificity of Its Aftereffects.

Image skew is one of the prominent distortions that exist in optical elements, such as in spectacle lenses. The present study evaluates adaptation to image skew in dynamic natural images. Moreover, the cortical levels involved in skew coding were probed using retinal specificity of skew adaptation aftereffects. Left and right skewed natural image sequences were shown to observers as adapting stimuli. The point of subjective equality (PSE), i.e., the skew amplitude in simple geometrical patterns that is perceived to be unskewed, was used to quantify the aftereffect of each adapting skew direction. The PSE, in a two-alternative forced choice paradigm, shifted toward the adapting skew direction. Moreover, significant adaptation aftereffects were obtained not only at adapted, but also at non-adapted retinal locations during fixation. Skew adaptation information was transferred partially to non-adapted retinal locations. Thus, adaptation to skewed natural scenes induces coordinated plasticity in lower and higher cortical areas of the visual pathway.

Perceptual suppression of predicted natural images.

Perception is shaped not only by current sensory inputs but also by expectations generated from past sensory experience. Humans viewing ambiguous stimuli in a stable visual environment are generally more likely to see the perceptual interpretation that matches their expectations, but it is less clear how expectations affect perception when the environment is changing predictably. We used statistical learning to teach observers arbitrary sequences of natural images and employed binocular rivalry to measure perceptual selection as a function of predictive context. In contrast to previous demonstrations of preferential selection of predicted images for conscious awareness, we found that recently acquired sequence predictions biased perceptual selection toward unexpected natural images and image categories. These perceptual biases were not associated with explicit recall of the learned image sequences. Our results show that exposure to arbitrary sequential structure in the environment impacts subsequent visual perceptual selection and awareness. Specifically, for natural image sequences, the visual system prioritizes what is surprising, or statistically informative, over what is expected, or statistically likely.

Columnar Machine: Fast estimation of structured sparse codes

Abstract Ever since the discovery of columnar structures, their function remained enigmatic. As a potential explanation for this puzzling function, we introduce the ‘Columnar Machine’. We join two neural network types, Structured Sparse Coding (SSC) of generative nature exploiting sparse groups of neurons and Feed-Forward Networks (FFNs) into one architecture. Memories supporting recognition can be quickly loaded into SSC via supervision or can be learned by SSC in a self-organized manner. However, SSC evaluation is slow. We train FFNs for predicting the sparse groups and then the representation is computed by fast undercomplete methods. This two step procedure enables fast estimation of the overcomplete group sparse representations. The suggested architecture works fast and it is biologically plausible. Beyond the function of the minicolumnar structure it may shed light onto the role of fast feed-forward inhibitory thalamocortical channels and cortico-cortical feed-back connections. We demonstrate the method for natural image sequences where we exploit temporal structure and for a cognitive task where we explain the meaning of unknown words from their contexts.

Humans perceive flicker artifacts at 500 Hz.

Humans perceive a stable average intensity image without flicker artifacts when a television or monitor updates at a sufficiently fast rate. This rate, known as the critical flicker fusion rate, has been studied for both spatially uniform lights, and spatio-temporal displays. These studies have included both stabilized and unstablized retinal images, and report the maximum observable rate as 50–90 Hz. A separate line of research has reported that fast eye movements known as saccades allow simple modulated LEDs to be observed at very high rates. Here we show that humans perceive visual flicker artifacts at rates over 500 Hz when a display includes high frequency spatial edges. This rate is many times higher than previously reported. As a result, modern display designs which use complex spatio-temporal coding need to update much faster than conventional TVs, which traditionally presented a simple sequence of natural images.

Temporal statistics of natural image sequences generated by movements with insect flight characteristics.

Many flying insects, such as flies, wasps and bees, pursue a saccadic flight and gaze strategy. This behavioral strategy is thought to separate the translational and rotational components of self-motion and, thereby, to reduce the computational efforts to extract information about the environment from the retinal image flow. Because of the distinguishing dynamic features of this active flight and gaze strategy of insects, the present study analyzes systematically the spatiotemporal statistics of image sequences generated during saccades and intersaccadic intervals in cluttered natural environments. We show that, in general, rotational movements with saccade-like dynamics elicit fluctuations and overall changes in brightness, contrast and spatial frequency of up to two orders of magnitude larger than translational movements at velocities that are characteristic of insects. Distinct changes in image parameters during translations are only caused by nearby objects. Image analysis based on larger patches in the visual field reveals smaller fluctuations in brightness and spatial frequency composition compared to small patches. The temporal structure and extent of these changes in image parameters define the temporal constraints imposed on signal processing performed by the insect visual system under behavioral conditions in natural environments.