Local Orientation Information Research Articles

The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images

Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond to linear stimuli in specific orientations, plays a pivotal role in both fields. In this study, we propose a novel orientation detection mechanism for local neurons based on dendrite computation, specifically designed for grayscale images. Our model comprises eight neurons capable of detecting local orientation information, with inter-neuronal interactions facilitated through nonlinear dendrites. Through the extraction of local orientation information, this mechanism effectively derives global orientation information, as confirmed by successful computer simulations. Experimental results demonstrate that our mechanism exhibits remarkable orientation detection capabilities irrespective of variations in size, shape, or position, which aligns with previous physiological research findings. These findings contribute to our understanding of the human visual system and provide valuable insights into both biological and computer vision. The proposed orientation detection mechanism, with its nonlinear dendritic computations, offers a promising approach for improving orientation detection in grayscale images.

Generative Sampling in Bundle Tractography using Autoencoders (GESTA).

Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail to map the true white matter pathways because some bundles are "harder-to-track" than others. This results in tractography reconstructions with poor white and gray matter spatial coverage. In this work, we propose a generative, autoencoder-based method, named GESTA (Generative Sampling in Bundle Tractography using Autoencoders), that produces streamlines achieving better spatial coverage. Compared to other deep learning methods, our autoencoder-based framework uses a single model to generate streamlines in a bundle-wise fashion, and does not require to propagate local orientations. GESTA produces new and complete streamlines for any given white matter bundle, including hard-to-track bundles. Applied on top of a given tractogram, GESTA is shown to be effective in improving the white matter volume coverage in poorly populated bundles, both on synthetic and human brain in vivo data. Our streamline evaluation framework ensures that the streamlines produced by GESTA are anatomically plausible and fit well to the local diffusion signal. The streamline evaluation criteria assess anatomy (white matter coverage), local orientation alignment (direction), and geometry features of streamlines, and optionally, gray matter connectivity. The GESTA framework offers considerable gains in bundle overlap using a reduced set of seeding streamlines with a 1.5x improvement for the "Fiber Cup", and 6x for the ISMRM 2015 Tractography Challenge datasets. Similarly, it provides a 4x white matter volume increase on the BIL&GIN callosal homotopic dataset, and successfully populates bundles on the multi-subject, multi-site, whole-brain in vivo TractoInferno dataset. GESTA is thus a novel deep generative bundle tractography method that can be used to improve the tractography reconstruction of the white matter.

Single-layer perceptron artificial visual system for orientation detection.

Orientation detection is an essential function of the visual system. In our previous works, we have proposed a new orientation detection mechanism based on local orientation-selective neurons. We assume that there are neurons solely responsible for orientation detection, with each neuron dedicated to detecting a specific local orientation. The global orientation is inferred from the local orientation information. Based on this mechanism, we propose an artificial visual system (AVS) by utilizing a single-layer of McCulloch-Pitts neurons to realize these local orientation-sensitive neurons and a layer of sum pooling to realize global orientation detection neurons. We demonstrate that such a single-layer perceptron artificial visual system (AVS) is capable of detecting global orientation by identifying the orientation with the largest number of activated orientation-selective neurons as the global orientation. To evaluate the effectiveness of this single-layer perceptron AVS, we perform computer simulations. The results show that the AVS works perfectly for global orientation detection, aligning with the majority of physiological experiments and models. Moreover, we compare the performance of the single-layer perceptron AVS with that of a traditional convolutional neural network (CNN) on orientation detection tasks. We find that the single-layer perceptron AVS outperforms CNN in various aspects, including identification accuracy, noise resistance, computational and learning cost, hardware implementation feasibility, and biological plausibility.

Orientation Detection System Based on Edge-Orientation Selective Neurons

In this paper, we propose a mechanism of orientation detection system based on edge-orientation selective neurons. We assume that there are neurons in the V1 that can generate response to object’s edge, and each neuron has the optimal response to specific orientation in a local receptive field. The global orientation is inferred from the aggregation of local orientation information. An orientation detection system is further developed based on the proposed mechanism. We design four types of neurons for four local orientations and used these neurons to extract local orientation information. The global orientation is obtained according to the neuron with the most activation. The performance of this orientation detection system is evaluated on orientation detection tasks. From the experiment results, we can conclude that our proposed global orientation mechanism is feasible and explainable. The mechanism-based orientation detection system shows better recognition accuracy and noise immunity than the traditional convolution neural network-based orientation detection systems and EfficientNet-based orientation detection system, which have the most accuracy for now. In addition, our edge-orientation selective cell based artificial visual system can greatly save time and learning cost compared to the traditional convolution neural network and EfficientNet.

Artificial Visual System for Orientation Detection Based on Hubel-Wiesel Model.

The Hubel–Wiesel (HW) model is a classical neurobiological model for explaining the orientation selectivity of cortical cells. However, the HW model still has not been fully proved physiologically, and there are few concise but efficient systems to quantify and simulate the HW model and can be used for object orientation detection applications. To realize a straightforward and efficient quantitive method and validate the HW model’s reasonability and practicality, we use McCulloch-Pitts (MP) neuron model to simulate simple cells and complex cells and implement an artificial visual system (AVS) for two-dimensional object orientation detection. First, we realize four types of simple cells that are only responsible for detecting a specific orientation angle locally. Complex cells are realized with the sum function. Every local orientation information of an object is collected by simple cells and subsequently converged to the corresponding same type complex cells for computing global activation degree. Finally, the global orientation is obtained according to the activation degree of each type of complex cell. Based on this scheme, an AVS for global orientation detection is constructed. We conducted computer simulations to prove the feasibility and effectiveness of our scheme and the AVS. Computer simulations show that the mechanism-based AVS can make accurate orientation discrimination and shows striking biological similarities with the natural visual system, which indirectly proves the rationality of the Hubel–Wiesel model. Furthermore, compared with traditional CNN, we find that our AVS beats CNN on orientation detection tasks in identification accuracy, noise resistance, computation and learning cost, hardware implementation, and reasonability.

Advanced process simulation of compression molded carbon fiber sheet molding compound (C-SMC) parts in automotive series applications

The following paper focuses on the development of a 3D virtual process chain (VPC) for parts made from Carbon Fiber Sheet Molding Compounds (C-SMC). The VPC consists of two modules addressing the compression molding and warpage simulation of parts made from two different high fiber volume fraction C-SMC materials. The press simulation module applies a solid mechanics material modelling approach within the Coupled Eulerian-Lagrangian (CEL) simulation framework available in ABAQUS/Explicit. Through a combination of flow characterization experiments and numerical simulations, local mechanical properties and fiber orientation information can be predicted from numerically simulated local flow strains. A warpage simulation module has also been developed in ABAQUS/Standard together with a data processing/transfer interface implemented in ANSA. With these highly integrated numerical tools, process characteristics such as press forces, flow fronts and weld-line development based on material amount, insert geometry and placement position in the mold can all be predicted and analyzed.

Integration of Probability Based Ridge Variation Information with Local Ridge Orientation for Fingerprint Liveness Detection

Fingerprints are commonly used in biometric systems. However, the authentication of these systems became an open challenge because fingerprints can easily be fabricated. In this paper, a hybrid feature extraction approach named Integration of Probability Weighted Spatial Gradient with Ridge Orientation (IPWSGRo) has been proposed for fingerprint liveness detection. IPWSGRo integrates intensity variation and local ridge orientation information. Intensity variation is computed by using probability-weighted moments (PWM) and second order directional derivative filter. Moreover, the ridge orientation is estimated using rotation invariant Local Phase Quantization (LPQri) by retaining only the significant frequency components. These two feature vectors are quantized into predefined intervals to plot a 2-D histogram. The support vector machine classifier (SVM) is then used to determine the validity of fingerprints as either live or spoof. Results are obtained by applying the proposed technique on three standard databases of LivDet competition 2011, 2013, and 2015. Experimental results indicate that the proposed method is able to reduce the average classification error rates (ACER) to 5.7, 2.1, and 5.17% on LivDet2011, 2013, and 2015, respectively.

Artificial Visual System for Orientation Detection

The human visual system is one of the most important components of the nervous system, responsible for visual perception. The research on orientation detection, in which neurons of the visual cortex respond only to a line stimulus in a particular orientation, is an important driving force of computer vision and biological vision. However, the principle underlying orientation detection remains a mystery. In order to solve this mystery, we first propose a completely new mechanism that explains planar orientation detection in a quantitative manner. First, we assume that there are planar orientation-detective neurons which respond only to a particular planar orientation locally and that these neurons detect local planar orientation information based on nonlinear interactions that take place on the dendrites. Then, we propose an implementation of these local planar orientation-detective neurons based on their dendritic computations, use them to extract the local planar orientation information, and infer the global planar orientation information from the local planar orientation information. Furthermore, based on this mechanism, we propose an artificial visual system (AVS) for planar orientation detection and other visual information processing. In order to prove the effectiveness of our mechanism and the AVS, we conducted a series of experiments on rectangular images which included rectangles of various sizes, shapes and positions. Computer simulations show that the mechanism can perfectly perform planar orientation detection regardless of their sizes, shapes and positions in all experiments. Furthermore, we compared the performance of both AVS and a traditional convolution neural network (CNN) on planar orientation detection and found that AVS completely outperformed CNN in planar orientation detection in terms of identification accuracy, noise resistance, computation and learning cost, hardware implementation and reasonability.

Selectivity for Local Orientation Information in Visual Mirror Symmetry Perception

Selectivity for Local Orientation Information in Visual Mirror Symmetry Perception

The Mechanism of Orientation Detection Based on Artificial Visual System

As an important part of the nervous system, the human visual system can provide visual perception for humans. The research on it is of great significance to improve our understanding of biological vision and the human brain. Orientation detection, in which visual cortex neurons respond only to linear stimuli in specific orientations, is an important driving force in computer vision and biological vision. However, the principle of orientation detection is still unknown. This paper proposes an orientation detection mechanism based on dendrite calculation of local orientation detection neurons. We hypothesized the existence of orientation detection neurons that only respond to specific orientations and designed eight neurons that can detect local orientation information. These neurons interact with each other based on the nonlinearity of dendrite generation. Then, local orientation detection neurons are used to extract local orientation information, and global orientation information is deduced from local orientation information. The effectiveness of the mechanism is verified by computer simulation, which shows that the machine can perform orientation detection well in all experiments, regardless of the size, shape, and position of objects. This is consistent with most known physiological experiments.

KPAM 2.0: Feedback Control for Category-Level Robotic Manipulation

In this letter, we explore generalizable, perception-to-action robotic manipulation for precise, contact-rich tasks. In particular, we contribute a framework for closed-loop robotic manipulation that automatically handles a category of objects, despite potentially unseen object instances and significant intra-category variations in shape, size and appearance. Previous approaches typically build a feedback loop on top of a realtime 6-DOF pose estimator. However, representing an object with a parameterized transformation from a fixed geometric template does not capture large intra-category shape variation. Hence we adopt the keypoint-based object representation proposed in [13] for category-level pick-and-place, and extend it to closed-loop manipulation policies with contact-rich tasks. We first augment keypoints with local orientation information. Using the oriented keypoints, we propose a novel object-centric action representation in terms of regulating the linear/angular velocity or force/torque of these oriented keypoints. This formulation is surprisingly versatile – we demonstrate that it can accomplish contact-rich manipulation tasks that require precision and dexterity for a category of objects with different shapes, sizes and appearances, such as peg-hole insertion for pegs and holes with significant shape variation and tight clearance. With the proposed object and action representation, our framework is also agnostic to the robot grasp pose and initial object configuration, making it flexible for integration and deployment. Video demonstration, source code and supplemental materials are available on https://sites.google.com/view/kpam2/home .

Impact of Microstrucutre on Dwell Fatigue in Dual-Phase Titanium Alloys

Dual phase titanium alloys, such as Ti-6242, experience a significant reduction in fatigue lifetime when the peak load is held at each cycle. This type of sustained peak loading, also known as dwell fatigue, mimics the long periods of high mean stress experienced by titanium fan and compressor components during takeoff and cruise. The reduction in fatigue lifetime is known as the dwell debit, and is attributed to the phenomenon of load shedding. Both local microstructure and temperature are known to impact load shedding and thereby the macroscopic response of Ti-6242 when subject to dwell fatigue, but the underlying mechanisms are still under active investigation. This study utilized electron backscatter diffraction (EBSD) and digital image correlation (DIC) to characterize the role of local microstructure and temperature on load shedding during dwell fatigue. EBSD was used to determine local orientation and texture information, and DIC provided information about the heterogeneity of the strain distribution and plastic strain accumulation. Ex-situ tests were performed to investigate the link between the deformation of local microstructures and macroscopic damage. The resultant strain fields and orientation maps were statistically analyzed to provide quantitative insights into the impact of local microstructure on load shedding during dwell fatigue.

Role of Orientation Processing in the Eggs Illusion.

In the eggs illusion, small patches perceptually deform when placed at the midpoints between the intersections of a regular grid. In this study, we explored the role of orientation processing in the illusion, by manipulating some spatiotemporal stimulus parameters using three psychophysical experiments. In Experiment 1, we manipulated grid luminance and presentation duration as independent variables and found that the illusion occurred even with a brief presentation of approximately 200 to 300 ms. In Experiments 2 and 3, besides presentation duration, we also systematically varied the orientation information of the stimulus. In addition to the original grid pattern, stimuli with only horizontal or vertical bars were employed in Experiment 2. The magnitude of the illusion was significantly weakened under the bar conditions. In Experiment 3, we varied the orientation of the bars stepwise and revealed that the local orientation information around the circular patches and the relative orientation information provided by the orthogonal bars of the grid are contributing factors to the illusion. Based on these results, we discussed the role of orientation processing in generating the eggs illusion.

Salient object detection via reciprocal function filter

Salient object detection (SOD) has been attracting a lot of interest, and recently many computational models have been developed. Here, the authors formulate a SOD model, in which saliency map is computed as a combination of the colour, its distribution-based saliency and orientation saliency. Similar to traditional SODs, the proposed method is based on super-pixel segmentation and super-pixel utilises both colour and its distribution-based saliency to generate a coarse saliency map. However, distinct from traditional SODs, authors further use orientation contrast to optimise the coarse saliency map to obtain an improved saliency map. Authors’ contributions are twofold. First, the authors combine colour uniqueness and its distribution with local orientation information (LOI) used in Itti's model to effectively improve profiles of salient regions. Second, a reciprocal function is defined to substitute the Gabor function used in LOI, and the authors have proved that the substitution could detect relatively homogeneous and uniform regions at the boundary of salient object, whereas it is what the traditional models lack. Authors’ approach significantly outperforms state-of-the-art methods on four benchmark datasets, while the authors demonstrate that the proposed method runs as fast as most existing algorithms.

Robust joint representation with triple local feature for face recognition with single sample per person

Face recognition (FR) with a single training sample per person (SSPP) is a representative small-sample-size classification problem and occurs in many practical scenarios such as law enforcement, surveillance, identity card, e-passport, etc. By using intra-class variations extracted from an additional training set (i.e., a set excluding gallery subjects) as a generic intra-class variation dictionary, sparse representation based classification (SRC) has been extended to FR with SSPP. However, for FR with SSPP, how to achieve high robustness to gross facial variations (e.g., complex facial lighting, expression and pose variations and various outliers of corruption, occlusion and disguise) is still an open issue. In this paper, we propose a novel model, named robust joint representation with triple local feature (RJR-TLF), to address this issue from the viewpoints of feature extraction and classifier design. In feature extraction, we design robust triple local features, i.e., Gabor facial features with multiple scales and multiple orientations extracted in different facial local regions (e.g., local patches centered around dense regularly sampled points and detected particular points including nose tip, eye centers, etc.), to naturally encode the local scale, local orientation and local space information of a face image. For face images, the densely and regularly sampled facial regions can provide a comprehensive description; the sparsely and particularly detected facial regions can exploit a discriminative description because they cover the most informative facial regions and can be detected robustly. In classifier design, we propose a robust joint representation framework to exploit the distinctiveness and similarity of different local information by requiring triple local features from the same type of Gabor feature (i.e., with the same scale and orientation) to have similar representation coefficients. With the coefficient-similarity constraint and the robust representation fidelity term representing the query image on the single-sample gallery set and the generic intra-class variation dictionary, the local features with large-representation residuals actually indicate corrupted regions with gross facial variations and will be assigned low weights adaptively to reduce their effects on the representation and classification, which further strengthens the robustness of RJR-TLF. The proposed RJR-TLF is evaluated extensively on popular databases, including the AR, the large-scale CMU Multi-PIE, and the LFW databases. Experimental results demonstrate that RJR-TLF is much more robust to various facial variations than the recent FR with SSPP methods.

Multi-Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

Biometrics based personal authentication has been found to be an effective method for recognizing, with high confidence, a person’s identity. With the emergence of reliable and inexpensive 3D scanners, recent years have witnessed a growing interest in developing 3D biometrics systems. As a commonsense, matching algorithms are crucial for such systems. In this paper, we focus on investigating identification methods for two specific 3D biometric identifiers, 3D ear and 3D palmprint. Specifically, we propose a Multi-Dictionary based Collaborative Representation (MDCR) framework for classification, which can reduce the negative effects aroused by some local regions. With MDCR, a range map is partitioned into overlapping blocks and, from each block, a feature vector is extracted. At the dictionary construction stage, feature vectors from blocks having the same locations in gallery samples can form a dictionary and, accordingly, multiple dictionaries are obtained. Given a probe sample, by coding its each feature vector on the corresponding dictionary, multiple class labels can be obtained and then we use a simple majority-based voting scheme to make the final decision. In addition, a novel patch-wise and statistics-based feature extraction scheme is proposed, combining the range image’s local surface type information and local dominant orientation information. The effectiveness of the proposed approach has been corroborated by extensive experiments conducted on two large-scale and widely-used benchmark datasets, the UND Collection J2 3D ear dataset and the PolyU 3D palmprint dataset. To make the results reproducible, we have publicly released the source code.

Local mean differential excitation pattern for content based image retrieval

Effective and facile local texture feature descriptors assume a significant role in many image classification and retrieval tasks. However, conventional feature descriptors are impaired to capture salient image features like local inherent structure, orientation and edge information of the image. A typical local binary pattern (LBP)-like feature descriptors elicits information based on the gray level difference from each locality and consequently its value immensely susceptible to noise. To overcome a few deficiencies of traditional methods, the proposed research work acquaint with a lucid, novel, yet robust texture feature descriptor called local mean differential excitation pattern (LMDeP) for efficient content based image retrieval. The main strategy of LMDeP is to elucidate differential excitation using the mean of points over each angular and radial neighbor points. This enables the LMDeP to fetch robust features by skimming the noise effect of enticing neighbors over each local patch. LMDeP performance compared to the LBP variants on Corel-1 K and Corel-5 K datasets establish the superiority of proposed method to its counterparts.

Poorer Integration of Local Orientation Information Occurs in Students With High Schizotypal Personality Traits.

Contour integration is impaired in schizophrenia patients, even at the first episode, but little is known about visual integration abilities prior to illness onset. To examine this issue, we compared undergraduate students high and low in schizotypal personality traits, reflecting putative liability to psychosis, on two psychophysical tasks assessing local and global stages of the integration process. The Radial Frequency Jittered Orientation Tolerance (RFJOT) task measures tolerance to orientation noise at the local signal level, when judging global stimulus orientation, whilst the Radial Frequency Integration Task (RFIT) measures the ability to globally integrate the local signals that have been extracted during shape discrimination. Positive schizotypy was assessed with the Perceptual Aberration (PAb) scale from the Wisconsin Schizotypy Scales-Brief. On the RFJOT task, the High PAb group (n = 55) tolerated statistically significantly less noise (d = −0.494) and had a lower proportion of correct responses (d = −0.461) than the Low PAb group (n = 77). For the RFIT there was no statistically significant difference in integration abilities between the High and Low PAb groups. High and Low PAb groups also differed on other positive and disorganized (but not negative) schizotypy traits, hence poorer performance on the RFJOT may not be solely related to unusual perceptual experiences. These findings suggest that difficulties with local noise tolerance but not global integration occur in healthy young adults with high levels of schizotypal personality traits, and may be worth investigating as a marker of risk for schizophrenia.

Hermite Snakes With Control of Tangents.

We introduce a new model of parametric contours defined in a continuous fashion. Our curve model relies on Hermite spline interpolation and can easily generate curves with sharp discontinuities; it also grants direct access to the tangent at each location. With these two features, the Hermite snake distinguishes itself from classical spline-snake models and allows one to address certain bioimaging problems in a more efficient way. More precisely, the Hermite snake construction allows introducing sharp corners in the snake curve and designing directional energy functionals relying on local orientation information in the input image. Using the formalism of spline theory, the model is shown to meet practical requirements such as invariance to affine transformations and good approximation properties. Finally, the dependence on initial conditions and the robustness to the noise is studied on synthetic data in order to validate our Hermite snake model, and its usefulness is illustrated on real biological images acquired using brightfield, phase-contrast, differential-interference-contrast, and scanning-electron microscopy.

The Face Inversion Effect: Roles of First- and Second-Order Configural Information.

The face inversion effect (FIE) is a reduction in recognition performance for inverted faces compared with upright faces. Several studies have proposed that a type of configural information, called second-order relational information, becomes more important with increasing expertise and gives rise to the FIE. However, recently it has been demonstrated that it is possible to obtain an FIE with facial features presented in isolation, showing that configural information is not necessary for this effect to occur. In this article we test whether there is a role for configural information in producing the FIE and whether second- or first-order relational information is particularly important. In Experiment 1, we investigated the role of configural information and local feature orientation by using a new type of "Thatcherizing" transformation on our set of faces, aiming to disrupt second-order and local feature orientation information but keeping all first-order properties unaltered. The results showed a significant reduction in the FIE for these "new" Thatcherized faces, but it did not entirely disappear. Experiment 2 confirmed the FIE for new Thatcherized faces, and Experiment 3 establishes that both local feature orientation and first-order relational information have a role in determining the FIE.