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Computer Vision and Machine Learning are the key to develop autonomous robots. While engaged with a IEEE Open Challenge, in which the robots need to recognize a miniature of a cow, we saw a solution in these areas. The main contribution of this paper is the algorithm implemented to identify and follow a known object, the miniature of a cow. We are constructing an application based on Image Processing...
Flexible printed circuit board (FPC) is a popular substrate for packaging integrated circuits (ICs). Detecting the circles rapidly on FPCs by using computer vision is very important to assess the quality of FPCs during its manufacturing. In this paper, a fast circle detection approach based on a threshold segmentation method and a validation check is proposed. In the algorithm, the image is firstly...
Owing to the elevated intra/inter variation among the foreground and background text of various document images, the text segmentation from the poorly degraded document images is the difficult job. This paper presents the document image binarization method by adaptive image contrast which is the integration of the local image gradient and the local image contrast which is lenient to background and...
Automatic parking systems have significant effects on intelligent transport systems (ITS) and have been extensively researched. However, most existing vision-based automatic parking slot detection methods cannot obtain the desired results due to variation in light intensity or complex obstacle conditions. Besides, most previous parking slot detection methods only consider the target position occupied...
There is a large demand in the area of video-surveillance, especially in people detection, which has caused a large increase in the number of researches and resources in this field. As training images and annotations are not always available, it is important to consider the cost involved in creating the detector models. For example, for elderly people detection, the detector must have into account...
This paper is devoted to traffic sign recognition problem in real time. The recognition process consists of three steps. The first step is a search of image parts which probably contain a traffic sign. The second step is about parts extraction and simple classification by shape. The last step consists of classification of extracted parts with previously learned multilayered neural net. The results...
Visual sensors are widely used in automatic parking systems, so this paper proposes an algorithm for the visual detection of available parking slots. The proposed system consists of two stages: parking slot recognition and slot occupancy classification. The parking slot recognition stage generates parking slots using the corner features of parking slot markings. The slot occupancy classification stage...
Road markings are important information of transport systems for drivers or intelligent vehicle. Efficient road markings feature extraction is pre-requisite to road markings detection, recognition and visual localization. However, most of previous lane markings feature extractors are operating on conventional images, the feature extraction methods for omnidirectional images are rarely considered in...
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective component in localization, but often computationally expensive and incapable of joint optimization with some of the remaining modules. In this paper, to the best of our knowledge, we for the first time integrate weakly supervised...
A honeycomb photodetector architecture is proposed for low-cost CMOS-compatible blue-enhanced sensing applications. In this paper, a physical model is developed to characterize the responsivity of honeycomb photodetectors. The model is then used to determine the optimum honeycomb physical dimensions for maximum responsivity and quantum efficiency. To implement the model in a real application, a test...
Learned boundary maps are known to outperform handcrafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is convolutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method...
Networks-on-chip (NoCs) have become a new chip design paradigm as the size of transistors continues to shrink. Globally-asynchronous locally-synchronous (GALS) on-chip networks are proposed for solving issues such as large clock tree distribution and signal delay variations. More interestingly, for the GALS networks using m-of-n delay-insensitive interconnect, the asynchronous interconnect not only...
This paper presents a timing error masking-aware ARM Cortex M0 microcontroller system. Timing errors are detected through a timing error detection strategy, consisting of a soft edge flip-flop combined with a transition detector and an error latch. The time borrowing realized through soft edge flip-flops allows data to propagate after the clock edge (timing error masking). Thus operation at the point-of-first-failure...
This article describes a completely new, fully automatic line detector algorithm that takes advantage of look-up tables to recognize and fit straight line patterns. The algorithm first recognizes any possible 4×4 pixel line patterns among the binary edge pixels and then uses several small look up tables to decide whether the connected patterns form a line or not. It is designed for real time processing...
From their beginning, people have always been prone to hiding something. Nowadays, the need for sending hidden messages has become the matter of pride and elite knowledge of managing how to send something to someone right in front of the eyes of others without others knowing about the communication. It opens a complete new perspective of the world, parallel to the existing one, where hidden messages...
The paper presents a new method of vehicle speed estimation using image data processing. The presented method employs conversion of greyscale input images into binary form. Image conversion into binary form is based on small gradients in the input images. Contents of the obtained binary images correspond with traffic scenes presented in the input images. Vehicle speed is estimated on the basis of...
Denoising digital images while preserving sharp details and fine edges is an active area of research. This paper presents novel local edge profile detection and preservation based denoising algorithm for digital images in presence of zero mean Gaussian noise. Detecting and preserving sharp changes in image pixel intensities preserves the visual quality of the denoised image. Twenty four different...
A background subtraction algorithm using an encoderdecoder structured convolutional neural network is proposed in this work, in order to segment out moving objects from the background. A target frame, its previous frame, and a background model are concatenated and fed into the network as the input. Then, the encoder generates a highlevel feature vector, and the decoder converts the feature vector...
We present a method of predictive reconstructing connections between parts of object outlines in images. The method was developed mainly to analyze microscopic medical images but is applicable to other types of images. Examined objects in such images are highly transparent, moreover close objects can overlap each other. Thus, segmentation and separation of such objects can be difficult. Another frequently...
Edge is the most used and important segmentation feature in most of the object based image processing applications. Primary challenging issues with all the edge detectors are their adaptability for different scenes, noise immunity and most importantly complexity of implementation which can hinder real time performance for high resolution images. In this paper, we have proposed a novel, efficient and...
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