Recognition of Partially Occluded Elliptical Objects using Symmetry on Contour

Over the past decades, object recognition has been predominantly studied and modelled as a feedforward process. This notion was supported by the fast response times in psychophysical and neurophysiological experiments and the recent success of deep feedforward neural networks for object recognition. Recently, however, this prevalent view has shifted and recurrent connectivity in the brain is now believed to contribute significantly to object recognition — especially under challenging conditions, including the recognition of partially occluded objects. Moreover, recurrent dynamics might be the key to understanding perceptual phenomena such as perceptual hysteresis. In this work we investigate if and how artificial neural networks can benefit from recurrent connections. We systematically compare architectures comprised of bottom-up, lateral, and top-down connections. To evaluate the impact of recurrent connections for occluded object recognition, we introduce three stereoscopic occluded object datasets, which span the range from classifying partially occluded hand-written digits to recognizing three-dimensional objects. We find that recurrent architectures perform significantly better than parameter-matched feedforward models. An analysis of the hidden representation of the models suggests that occluders are progressively discounted in later time steps of processing. We demonstrate that feedback can correct the initial misclassifications over time and that the recurrent dynamics lead to perceptual hysteresis. Overall, our results emphasize the importance of recurrent feedback for object recognition in difficult situations.

이 논문에서 겹쳐지고 잘린 이미지내의 타원형 객체들 가운데 부분적으로 겹쳐져 보이지 않는 외형과 영역을 재구성하고 계산하기 위한 방법을 제안한다. 대칭적인 속성에 기반을 두고, 불완전한 객체 인식을 위해 타원형 객체의 윤곽에 기반을 둔 방법이다. 이 방법은 한 객체 안에서 대칭 축을 이용하는 영역 복사를 통한 겹쳐져 보이지 않는 영역을 재구성하는 간결한 기교를 제공한다. 부분적으로 겹쳐져 보이지 않는 영역에 대한 측정된 변수에 기반을 두고, 분류 트리의 객체 인지를 수행하는데, 이 방법은 통계 수치보다 대칭에 기반을 둔 객체 재구성에 의존하기 때문이다. 이는 크기 변경과, 객체의 자세, 회전, 등에서 비록 객체 자세에는 한계를 가지고 있지만 부분적으로 겹쳐져 보이지 않는 객체의 인지에서 탁월하다. In This Paper, We Propose The Method To Reconstruct And Estimate Partially Occluded Elliptical Objects In Images From Overlapping And Cutting. We Present The Robust Method For Recognizing Partially Occluded Objects Based On Symmetry Properties, Which Is Based On The Contours Of Elliptical Objects. A Proposed Method Provides Simple Techniques To Reconstruct Occluded Regions Via A Region Copy Using The Symmetry Axis Within An Object. Based On The Estimated Parameters For Partially Occluded Objects, We Perform Object Recognition On The Classifier. Since A Proposed Method Relies On Reconstruction Of The Object Based On The Symmetry Properties Rather Than Statistical Estimates, It Has Proven To Be Remarkably Robust In Recognizing Partially Occluded Objects In The Presence Of Scale Changes, Object Pose, And Rotated Objects With Occlusion, Even Though h Proposed Method Has Minor Limitations Of Object Poses.

This paper introduces the technique of graph cut into the extraction of object region and applies the corresponding result of object region extraction into the image retrieval based on object region. The main idea of image retrieval based on object region is to use the feature of object region instead of the feature of global image to participate in the image retrieval. In the field of graphics there is a technique called graph cut, which can be used to figure out the contour of object under the interaction of users. The graph cut algorithm can be used to verify the correctness of object region extraction, and the users' input about seeds can be simulated according to the initial object region extracted. The usage of graph cut can make the object region extracted more precisely and thus the performance of image retrieval based on object region can be improved. Experiments show that the object region extraction algorithm based on graph cut is valid and the subsequent image retrieval results accord with the human visual perception much more than the ones without the usage of graph cut.

A Review of Recent Advancements in Appearance-based Object Recognition

Identification of objects in a real world plays a key role for human-computer interaction in a computer-augmented environment using augmented reality techniques. For providing natural interaction in such environments, it is necessary for an interface system to know which objects a user is using. In the previously developed interface systems, real objects are identified by using specially designed tags attached to objects. In this work, we propose a new method for interactive object recognition and registration for more natural and intuitive interaction without using any tags. In particular, we introduce interactive object registration and recognition by combining direct manipulation with user's hands and a color-based object recognition algorithm.

Object detection has received significant interest in the research field of computer vision and is widely used in human-centric applications. The occlusion problem is a frequent obstacle that degrades detection quality. In this paper, we propose a novel object detection framework targeting robust object detection in occlusion. The proposed deep learning-based network consists mainly of two parts: 1) object detection framework, which classifies the object categories and localizes the object location and 2) plug-in bounding-box (BB) estimator, which estimates the object and occlusion region from the feature map of the backbone network and the corresponding critic network for evaluating the predicted BB map. The BB estimator and the critic network are the plug-in modules added to the object detection framework and learned competitively with adversarial manner. As the plug-in BB estimator is learned to estimate the BB map containing the object and occlusion pattern information, the backbone network can embed this information to enable robust detection under occlusion in the test phase. The comprehensive experimental results on the PASCAL VOC, MS COCO, and KITTI dataset showed that the performance is improved with the plug-in BB-Critic network by predicting and criticizing object and occlusion in general generic object detection framework.

Three-month-old infants’ object recognition across changes in viewpoint using an operant learning procedure

Image classification and retrieval has significant importance in a wide variety of applications like object recognition, tracking, and content based retrieval, etc. Images usually consist of various objects which are segmented and then analysed for object-based classification and recognition. Owing to the absence of intensity and colour information, binary objects are difficult to recognise. They are usually represented using compact, geometrically invariant and robust features extracted from the object's contour or interior region. These features form the basis for recognition and govern the overall performance of classification systems. A new shape signature is introduced in this paper for representing shapes through angular profiles signature which are extracted from objects enclosed within minimum bounding circles. We have evaluated the discriminatory capabilities of this signature in shape recognition and retrieval on two shape datasets. Experimental results indicate that the signature is able to represent shapes effectively, achieving overall accuracy of 94%.

This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering L feature points on the test image with N feature points of the reference image is of order O(N x L). The method has been tested on real indoor and outdoor images and performs well.

A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world. These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space–time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system. Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales. It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators. Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation. Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations. There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision. Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space–time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space–time tilted receptive fields in V1, all within the same unified theory. In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli. This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision. It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms.

Human object classification in video is an important problem in computer vision. The main challenge is to correctly classify any human object in presence of several objects, occlusion of object, varying background and lighting conditions, etc. Object classification is much desired in surveillance like applications. Several classification algorithms have been proposed in spatial and wavelet domain. In this paper we present an object classification application of machine learning which is based on the single feature of object. The feature chosen for classification is energy of biorthogonal wavelet transform (BWT) coefficients of the object and is used to classify the object in a video into two categories: human object and non-human object. Two important properties of biorthogonal wavelet transform - shift invariance and symmetry, are useful for object classification. Translation in object is well handled by the shift invariance property while symmetry property is used to maintain the object boundaries. Classification has been performed using Adaboost classifier. Quantitative analysis of the results demonstrates better performance of the proposed method over other state-of-the-art methods.

An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method, and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally, an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake, and by using K-means and the EPF algorithm, multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.

New shape descriptor in the context of edge continuity

To realize object recognition in practical applications, it is necessary to handle layered objects. In this paper we introduce RGB-D sensor for object recognition system to treat overlapped objects. In the proposed method, we divide objects into segments and merge them by consulting partially recognition scheme. By several experiments, out method can recognize 30% occluded object with enough accuracy.

Tracking occluded objects at different depths has become as extremely important component of study for any video sequence having wide applications in object tracking, scene recognition, coding, editing the videos and mosaicking. This paper experiments with the capabilities of image annotation contour based tracking for occluded object. Image annotation is applied on 3 similar normal video sequences varying in depth. In the experiment, one bike occludes the other at a depth of 60 cm, 80 cm and 100 cm respectively. The effect on tracking is also analyzed with illumination variations using 3 different light sources in video sequences having objects occluding one another at same depth. The paper finally studies the ability of annotation to track the occluded object based on pyramids with variation in depth further establishing a threshold at which the ability of the system to track the occluded object fails. The contour of both the individual objects cant be tracked due to the distortion caused by overlapping of the object pyramids. The thresholds established can be used as a bench mark to estimate the capability of different softwares. The paper further computes the frame by frame error incurred by the system, supported by detailed simulations. This system can be effectively used to achieve flawless tracking as the error in motion tracking can be corrected. This can be of great interest to computer scientists while designing surveillance systems etc.