Vision systems are widely adopted in industrial fields for monitoring and automation. As a typical example, industrial vision systems are extensively implemented in vibrator parts feeder to ensure orientations of parts for assembling are aligned and disqualified parts are eliminated. An efficient parts orientation recognition and counting method is thus critical to adopt. In this paper, an integrated method for fast parts counting and orientation recognition using industrial vision systems is proposed. Original 2D spatial image signal of parts is decomposed to 1D signal with its temporal variance, thus efficient recognition and counting is achievable, feeding speed of each parts is further leveraged to elaborate counting in an adaptive way. Experiments on parts of different types are conducted, the experimental results revealed that our proposed method is both more efficient and accurate compared to other relevant methods.

In this paper, we present a high frame rate (HFR) vision system that can automatically control its exposure time by executing brightness histogram-based image processing in real time at a high frame rate. Our aim is to obtain high-quality HFR images for robust image processing of high-speed phenomena even under dynamically changing illumination, such as lamps flickering at 100 Hz, corresponding to an AC power supply at 50 / 60 Hz. Our vision system can simultaneously calculate a 256-bin brightness histogram for an 8-bit gray image of 512×512 pixels at 2000 fps by implementing a brightness histogram calculation circuit module as parallel hardware logic on an FPGA-based high-speed vision platform. Based on the HFR brightness histogram calculation, our method realizes automatic exposure (AE) control of 512×512 images at 2000 fps using our proposed AE algorithm. The proposed AE algorithm can maximize the number of pixels in the effective range of the brightness histogram, thus excluding much darker and brighter pixels, to improve the dynamic range of the captured image without over- and under-exposure. The effectiveness of our HFR system with AE control is evaluated according to experimental results for several scenes with illumination flickering at 100 Hz, which is too fast for the human eye to see.

In this paper, we propose a quantitative metric of measuring the degree of the visual fatigue in a stereoscopy. To the best of our knowledge, this is the first simplified relative quantitative approach describing visual fatigue value of a stereoscopy. Our experimental result shows that the correlation index of more than 98% is obtained between our Simplified Relative Visual Fatigue (SRVF) model and Mean Opinion Score (MOS).

Vision sensors provide rich sources of information, but sensing images and processing them in real time would be a challenging task. This paper introduces a vision system using SoCBase platform and presents heuristic designs of SAD correlation algorithm as a component of the vision system. Simulation results show that the vision system is suitable for real-time applications and that the heuristic designs of SAD algorithm are worth utilizing since they save a considerable amount of space with little sacrificing in quality.

Image enhancement plays an important role in many machine vision applications on images captured in low contrast and low illumination conditions. In this study, we propose a new method for image enhancement based on analysis on embedded surfaces of images. The proposed method gives an insight into the relationship between the image intensity and image enhancement. In our method, scaled surface area and the surface volume are proposed and used to reconstruct the image iteratively for contrast enhancement, and the illumination of the reconstructed image can also be adjusted simultaneously. On the other hand, the most common methods for measuring the quality of enhanced images are Mean Square Error (MSE) or Peak Signal-to-Noise-Ratio (PSNR) in conventional works. The two measures have been recognized as inadequate ones because they do not evaluate the result in the way that the human vision system does. This paper also presents a new framework for evaluating image enhancement using both objective and subjective measures. This framework can also be used for other image quality evaluations such as denoising evaluation. We compare our enhancement method with some well-known enhancement algorithms, including wavelet and curvelet methods, using the new evaluation framework. The results show that our method can give better performance in most objective and subjective criteria than the conventional methods.

In this paper, we describe a method for estimating external camera parameters in real time. We investigated the effectiveness of this method for annotating real scenes with 3-D virtual objects on a wearable computer. The proposed method enables determining known natural feature points of objects through multiplied color histogram matching and template matching. This external-camera-parameter calculation method consists of three algorithms for PnP problems, and it uses each algorithm selectively. We implemented an experimental system based on our method on a wearable vision system. This experimental system can annotate real objects with 3D virtual objects by using the proposed method. The system was implemented in order to enable effective annotation in a mixed-reality environment on a wearable computing system. The system consists of an ultra small CCD camera set at the user's eye, an ultra small display, and a computer. This computer uses the proposed method to determine the camera parameters. It then renders virtual objects based on the camera parameters and synthesizes images on a display. The system works at 10 frames per second.

We propose a new active vision system that mimics a saccadic movement of human eye. It is implemented based on a new computational model using neural networks. In this model, the visual pathway was divided in order to categorize a saccadic eye movement into three parts, each of which was then individually modeled using different neural networks to reflect a principal functionality of brain structures related with the saccadic eye movement in our brain. Initially, the visual cortex for saccadic eye movements was modeled using a self-organizing feature map, then a modified learning vector quantization network was applied to imitate the activity of the superior colliculus relative to a visual stimulus. In addition, a multilayer recurrent neural network, which is learned by an evolutionary computation algorithm, was used to model the visual pathway from the superior colliculus to the oculomotor neurons. Results from a computer simulation show that the proposed computational model is effective in mimicking the human eye movements during a saccade. Based on the proposed model, an active vision system using a CCD type camera and motor system was developed and demonstrated with experimental results.

Computational sensor (smart sensor, vision chip in other words) is a very small integrated system, in which processing and sensing are unified on a single VLSI chip. It is designed for a specific targeted application. Research activities of computational sensor are described in this paper. There have been quite a few proposals and implementations in computational sensors. Firstly, their approaches are summarized from several points of view, such as advantage vs. disadvantage, neural vs. functional, architecture, analog vs. digital, local vs. global processing, imaging vs. processing, new processing paradigms. Then, several examples are introduced which are spatial processings, temporal processings, A/D conversions, programmable computational sensors. Finally, the paper is concluded.

This paper describes a vision system with dual viewing angles, i. e., wide and narrow viewing angles, and a scheme of user-friendly speech dialogue environment based on the vision system. The wide viewing angle provides a wide viewing field for wide range motion tracking, and the narrow viewing angle is capable of following a target in wide viewing field to take the image of the target with sufficient resolution. For a fast and robust motion tracking, modified motion energy (MME) and existence energy (EE) are defined to detect the motion of the target and extract the motion region at the same time. Instead of using a physical device such as a foot switch commonly used in speech dialogue systems, the begin/end of an utterance is detected from the movement of user's mouth in our system. Without recognizing the movement of lips directly, the shape variation of the region between lips is tracked for more stable recognition of the span of a dialogue. The tracking speed is about 10 frames/sec when no recognition is performed and about 5 frames/sec when both tracking and recognition are performed without using any special hardware.

We previously proposed a robust hybrid edge detector which relaxes the trade off between robustess against noise and accurate localization of the edges. This hybrid detector separates the tasks of localization and noise suppresion between two sub-detectors. In this paper, we present an extension to this hybrid detector to determine its optimal parameters, independently of the scene. This extension defines a probabilistic cost function using for criteria the probability of missing an edge buried in noise and the probability of detecting false edges. The optimization of this cost function allows the automatic selection of the parameters of the hybrid edge detector given the height of the minimum edge to be detected and the variance of the noise, σ2n. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors.

Within the framework of a previously proposed vision system, a new part-segmentation algorithm, that breaks an object defined by its contour into its constituent parts, is presented. The contour is assumed to be obtained using an edge detector. This decomposition is achieved in two stages. The first stage is a preprocessing step which consists of extracting the convex dominant points (CDPs) of the contour. For this aim, we present a new technique which relaxes the compromise that exists in most classical methods for the selection of the width of the Gaussian filter. In the subsequent stage, the extracted CDPs are used to break the object into convex parts. This is performed as follows: among all the points of the contour only the CDPs are moved along their normals nutil they touch another moving CDP or a point on the contour. The results show that this part-segmentation algorithm is invariant to transformations such as rotation, scaling and shift in position of the object, which is very important for object recognition. The algorithm has been tested on many object contours, with and without noise and the advantages of the algorithm are listed in this paper. Our results are visually similar to a human intuitive decomposition of objects into their parts.

To describe the state of visual communications in the U.S., two words come to mind: digital and anticipation. Although compressed, digital video has been used in teleconferencing systems for at least ten years, it is only recently that a broad consensus has developed among diverse industries anticipating business opportunities, value, or both in digital video. The drivers for this turning point are: advances in digital signal processing, continued improvement in the cost, complexity, and speed of VLSI, maturing international standards and their adoption by vendors and end users, and a seemingly insatiable consumer demand for greater diversity, accessibility, and control of communication systems.