The effect of transparency on recognition of overlapping objects

Are overlapping objects easier to recognize when the objects are transparent or opaque? It is important to know whether the transparency of X-ray images of luggage contributes to the difficulty in searching those images for targets. Transparency provides extra information about objects that would normally be occluded but creates potentially ambiguous depth relations at the region of overlap. Two experiments investigated the threshold durations at which adult participants could accurately name pairs of overlapping objects that were opaque or transparent. In Experiment 1, the transparent displays included monocular cues to relative depth. Recognition of the back object was possible at shorter durations for transparent displays than for opaque displays. In Experiment 2, the transparent displays had no monocular depth cues. There was no difference in the duration at which the back object was recognized across transparent and opaque displays. The results of the two experiments suggest that transparent displays, even though less familiar than opaque displays, do not make object recognition more difficult, and possibly show a benefit. These findings call into question the importance of edge junctions in object recognitio

Automatic Color Inspection for Colored Wires in Electric Cables

In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions

Left/Right Hand Segmentation in Egocentric Videos

Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision methods handle hand segmentation as a background-foreground problem, ignoring two important facts: i) hands are not a single "skin-like" moving element, but a pair of interacting cooperative entities, ii) close hand interactions may lead to hand-to-hand occlusions and, as a consequence, create a single hand-like segment. These facts complicate a proper understanding of hand movements and interactions. Our approach extends traditional background-foreground strategies, by including a hand-identification step (left-right) based on a Maxwell distribution of angle and position. Hand-to-hand occlusions are addressed by exploiting temporal superpixels. The experimental results show that, in addition to a reliable left/right hand-segmentation, our approach considerably improves the traditional background-foreground hand-segmentation

A microcomputer-based vision system to recognize and locate partially occluded parts in binary and gray level images

This paper presents a microcomputer-based machine vision system to recognize and locate partially occluded parts in binary or gray level images. The recognition process is restricted to untilted, two-dimensional objects;A new edge-tracking technique in conjunction with a straight-line approximation algorithm is used to identify the local features in an image. Corners and holes serve as local features. The local features identified in an image are matched against all the compatible features stored for the model parts. The algorithm computes, for all image and model features matches, a coordinate transformation that maps a model feature onto an image feature. A new clustering algorithm has been developed to identify consistent coordinate transformation clusters that serve as initial match hypotheses. A hypothesis verification process eliminates the match hypotheses that are not compatible with the image information;The system performance was compared to a vision system restricted to recognize nonoverlapping parts. Both systems require the same hardware configuration and share the basic image processing routines

Disparity map generation based on trapezoidal camera architecture for multiview video

Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities, the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video remains a huge challenge. This paper presents the mathematical description of trapezoidal camera architecture and relationships which facilitate the determination of camera position for visual content acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera Architecture is that it allows for adaptive camera topology by which points within the scene, especially the occluded ones can be optically and geometrically viewed from several different viewpoints either on the edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate description could very well be used to address the issue of occlusion which continues to be a major problem in computer vision with regards to the generation of depth map