Irregular Shape Of Object Research Articles

Object Detection and Tracking in Real Time Video

Object tracking is the process of locating moving objects over time using the camera in video sequences. The objective of object tracking is to associate target objects in consecutive video frames. Object tracking requires the location and shape or features of objects in the video frames. So, object detection and object classification are the preceding steps of object tracking in computer vision applications. To detect or locate the moving object in the frame, Object detection is the first stage in tracking. It is a challenging or difficult task in image processing to track the objects into consecutive frames. Various challenges can arise due to complex object motion, irregular shape of object, occlusion of object to object and object to the scene, and real-time processing requirements. This paper presents the various techniques of object tracking in video sequences.

A Novel Framework for Improving Pulse-Coupled Neural Networks With Fuzzy Connectedness for Medical Image Segmentation

A pulse-coupled neural network (PCNN) is a promising image segmentation approach that requires no training. However, it is challenging to successfully apply a PCNN to medical image segmentation due to common but difficult scenarios such as irregular object shapes, blurred boundaries, and intensity inhomogeneity. To improve this situation, a novel framework incorporating fuzzy connectedness ( FC ) is proposed. First, a comparative study of the traditional PCNN models is carried out to analyze the framework and firing mechanism. Then, the characteristic matrix of fuzzy connectedness ( CMFC ) is presented for the first time. The CMFC can provide more intensity information and spatial relationships at the pixel level, which is helpful for producing a more reasonable firing mechanism in the PCNN models. Third, by integrating the CMFC into the PCNN framework models, a construction scheme of FC-PCNN models is designed. To illustrate this concept, a general solution that can be applied to different PCNN models is developed. Next, the segmentation performances of the proposed FC-PCNN models are evaluated by comparison with the traditional PCNN models, the traditional segmentation methods, and deep learning methods. The test images include synthetic and real medical images from the Internet and three public medical image datasets. The quantitative and visual comparative analysis demonstrates that the proposed FC-PCNN models outperform the traditional PCNN models and the traditional segmentation methods and achieve competitive performance to the deep learning methods. In addition, the proposed FC-PCNN models have favorable capability to eliminate inference from surrounding artifacts.

RETRACTED: An investigation of various actuation mechanisms in robot arm

Many research activities have been carried out to develop a simple mechanism for grasping irregular object shapes using two- or three-fingered robot end effectors. The idea behind this work is to develop three-fingered intelligent grippers that are capable of sensing different factors like weight, effort required, compactness, robustness, and stability of the object held during the manipulations. In this paper, five different actuation mechanisms, namely, edge-cam-operated actuation mechanism, toggle-linkage-based actuation mechanism, wedge-cam-operated gripper, sliding slotted pin–ball joint arrangement, and rack-and-pinion-operated four-bar linkage mechanism, are introduced. The actuation and grasping force of the gripper are to be determined using the analytical approach (static force analysis). Finally, the effective intelligent gripper mechanism is identified based on grasping force for grasping 1 kg weight of a prespecified object.

Pre-Charged Pneumatic Soft Gripper With Closed-Loop Control

Pneumatic soft grippers have nonlinear continuum deformation enabling them to adapt to irregular object shapes. Most of the existing pneumatic soft grippers use only open-loop control. Attempts for closed-loop control of soft grippers are all based on air pressure regulation, which is inaccurate and cumbersome due to nonlinear performance of the soft actuator and the compressible nature of air. In this letter, we present a controllable soft gripper based on pre-charged pneumatic soft actuators. The soft actuators, with the same design as a normal bending pneumatic soft actuator, are pre-charged with air to a preset pressure through a one-way check valve. The bending angle and bending speed of soft actuators are controlled by servomotors through tendons based on feedback data from force and proximity sensors. Kinematic models of the soft actuators are developed. Dynamic properties are experimentally studied. A prototype gripper with closed-loop control is developed for controllable grasping demonstration.

Surface diagnosticity predicts the high-level representation of regular and irregular object shape in human vision

The human visual system has an extraordinary capacity to compute three-dimensional (3D) shape structure for both geometrically regular and irregular objects. The goal of this study was to shed new light on the underlying representational structures that support this ability. Observers (N = 85) completed two complementary perceptual tasks. Experiment 1 involved whole–part matching of image parts to whole geometrically regular and irregular novel object shapes. Image parts comprised either regions of edge contour, volumetric parts, or surfaces. Performance was better for irregular than for regular objects and interacted with part type: volumes yielded better matching performance than surfaces for regular but not for irregular objects. The basis for this effect was further explored in Experiment 2, which used implicit part–whole repetition priming. Here, we orthogonally manipulated shape regularity and a new factor of surface diagnosticity (how predictive a single surface is of object identity). The results showed that surface diagnosticity, not object shape regularity, determined the differential processing of volumes and surfaces. Regardless of shape regularity, objects with low surface diagnosticity were better primed by volumes than by surfaces. In contrast, objects with high surface diagnosticity showed the opposite pattern. These findings are the first to show that surface diagnosticity plays a fundamental role in object recognition. We propose that surface-based shape primitives—rather than volumetric parts—underlie the derivation of 3D object shape in human vision.

Object tracking via Online Multiple Instance Learning with reliable components

Visual object tracking is a challenging and essential research problem in the field of computer vision. Recent years, many Online Multiple Instance Learning (MIL) tracking methods have been proposed with promising experimental results. These methods train a discriminative classifier under the boosting framework. The weak classifiers are learned from parts of the object and all classifiers are updated while updating the appearance model. However, due to irregular shape of object or occlusions, some components are not on object and should not be learned. On the contrary, a discriminative weak classifier learned from these components will mislead the tracker to drift away. To overcome this problem, we propose a novel online MIL tracking approach by updating with reliable components (OMRC). It keeps both background and object templates while tracking. By comparing current tracking result with two templates, we can get whether the pixels belong to object. The components which have a higher rate of pixels belong to object than a predefined threshold are reliable components. Moreover, in order to represent images better, we use HOG features and Histogram features instead of the widely used Haar-like features and design a new online weak classifier learning method. Experiments are performed on two challenging datasets including OTB2015 and Temple Color. Experimental results demonstrate the robustness of our OMRC tracker and the effectiveness of each component in the OMRC tracker.

Loop-free snakes for highly irregular object shapes

`Snakes' are an effective approach to image segmentation. However, self-looping is a common problem that can cause segmentation failure of snakes in the recovery of highly irregular object shapes, such as in long tube-like shapes, sharp corners or deep concave/convex shapes. This paper introduces the notion of loop-free snakes that can quickly and effectively remove all self-loops during their evolution, consistently deforming and conforming to complicated shapes of target objects. The proposed snakes are less sensitive to their initial contour condition, are resilient to their inconsistent parameter settings in a certain degree and require low computing cost in terms of both computation time and storage. Experiments are conducted to segment real images with encouraging results.