The Perceptual Genesis of Near Versus Far in Pictorial Stimuli

The experiments reported herein probe the visual cortical mechanisms that control near-far percepts in response to two-dimensional stimuli. Figural contrast is found to be a principal factor for the emergence of percepts of near versus far in pictorial stimuli, especially when stimulus duration is brief. Pictorial factors such as interposition (Experiment 1) and partial occlusion (Experiments 2 and 3) may cooperate or compete with contrast factors, in the manner predicted by the FACADE model. In particular, if the geometrical configuration of an image favors activation of cortical bipole grouping cells, as at the top of aT-junction, then this advantage can cooperate with the contrast of the configuration to facilitate a near-far percept at a lower contrast than at an X-junction. The more balanced bipole competition in the X-junction case takes longer to resolve than in the T-junction case (Experiment 3).Human Frontier Science Program Organization (SF9/98); Defense Research Projects Agency and the Office of Naval Research (N00014-92-J-I309, N00014-95-1-0494, N00014-95-1-0657

The Perceptual Genesis of Near Versus Far in Pictorial Stimuli

The experiments reported herein probe the visual cortical mechanisms that control near-far percepts in response to two-dimensional stimuli. Figural contrast is found to be a principal factor for the emergence of percepts of near versus far in pictorial stimuli, especially when stimulus duration is brief. Pictorial factors such as interposition (Experiment 1) and partial occlusion (Experiments 2 and 3) may cooperate or compete with contrast factors, in the manner predicted by the FACADE model. In particular, if the geometrical configuration of an image favors activation of cortical bipole grouping cells, as at the top of aT-junction, then this advantage can cooperate with the contrast of the configuration to facilitate a near-far percept at a lower contrast than at an X-junction. The more balanced bipole competition in the X-junction case takes longer to resolve than in the T-junction case (Experiment 3).Human Frontier Science Program Organization (SF9/98); Defense Research Projects Agency and the Office of Naval Research (N00014-92-J-I309, N00014-95-1-0494, N00014-95-1-0657

Reno-mesentero-aorto-iliac thromboendarterectomy in patient with malignant hypertension

1. 1. A case is documented in which there was complete occlusion of the left renal artery and partial occlusion of the right renal and superior mesenteric arteries, complicating extensive aortoiliac thrombosis. 2. 2. The patient presented with malignant hypertension, intermittent claudication, and abdominal complaints suggestive of "intestinal angina." 3. 3. Treatment consisted of thromboendarterectomy of all involved vessels, following which the patient became normotensive and had cessation of the abdominal symptoms. 4. 4. The case is thought to be the first successful bilateral simultaneous renal endarterectomy, and the third successful case of superior mesenteric endarterectomy. © 1959

DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion

In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer the learned knowledge to deal with occlusions. This setting alleviates the difficulty in collecting an exponentially large dataset to cover occlusion patterns and is more essential. In this scenario, the proposal-based deep networks, like RCNN-series, often produce unsatisfactory results, because both the proposal extraction and classification stages may be confused by the irrelevant occluders. To address this, [25] proposed a voting mechanism that combines multiple local visual cues to detect semantic parts. The semantic parts can still be detected even though some visual cues are missing due to occlusions. However, this method is manually-designed, thus is hard to be optimized in an end-to-end manner. In this paper, we present DeepVoting, which incorporates the robustness shown by [25] into a deep network, so that the whole pipeline can be jointly optimized. Specifically, it adds two layers after the intermediate features of a deep network, e.g., the pool-4 layer of VGGNet. The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts. We also propose an improved version DeepVoting+ by learning visual cues from context outside objects. In experiments, DeepVoting achieves significantly better performance than several baseline methods, including Faster-RCNN, for semantic part detection under occlusion. In addition, DeepVoting enjoys explainability as the detection results can be diagnosed via looking up the voting cues

On Shape-Mediated Enrolment in Ear Biometrics

Ears are a new biometric with major advantage in that they appear to maintain their shape with increased age. Any automatic biometric system needs enrolment to extract the target area from the background. In ear biometrics the inputs are often human head profile images. Furthermore ear biometrics is concerned with the effects of partial occlusion mostly caused by hair and earrings. We propose an ear enrolment algorithm based on finding the elliptical shape of the ear using a Hough Transform (HT) accruing tolerance to noise and occlusion. Robustness is improved further by enforcing some prior knowledge. We assess our enrolment on two face profile datasets; as well as synthetic occlusion

Cell nuclei detection using globally optimal active contours with shape prior

Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail

Facial Expression Analysis under Partial Occlusion: A Survey

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM Computing Surveys (accepted on 02-Nov-2017