Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network

Abstract: Detection of helmeted and non-helmeted motorcyclist is mandatory now-a-days in order to ensure the safety of riders on the road. However, due to many constraints such as poor video quality, occlusion, illumination, and other varying factors it becomes very difficult to detect them accurately. In this paper, we introduce an approach for automatic detection of helmeted and non-helmeted motorcyclist using convolutional neural network (CNN). During the past several years, the advancements in deep learning models have drastically improved the performance of object detection. One such model is YOLOv2 [1] which combines both classification and object detection in a single architecture. Here, we use YOLOv2 at two different stages one after another in order to improve the helmet detection accuracy. At the first stage, YOLOv2 model is used to detect different objects in the test image. Since this model is trained on COCO dataset, it can detect all classes of the COCO dataset. In the proposed approach, we use detection of person class instead of motorcycle in order to increase the accuracy of helmet detection in the input image. The cropped images of detected persons are used as input to second YOLOv2 stage which was trained on our dataset of helmeted images. The non-helmeted images are processed further to extract license plate by using OpenALPR. In the proposed approach, we use two different datasets i.e., COCO and helmet datasets. We tested the potential of our approach on different helmeted and non-helmeted images. Experimental results show that the proposed method performs better when compared to other existing approaches with 94.70% helmet detection accuracy.

Off-line unconstrained Farsi handwritten word recognition using fuzzy vector quantization and hidden Markov word models

Abstract: An unconstrained Farsi handwritten word recognition system based on fuzzy vector quantization (FVQ) and a hidden Markov model (HMM) for reading city names in postal addresses is presented. Preprocessing techniques including binarization, noise removal, slope correction and baseline estimation are described. Each word image is represented by its contour information. The histogram of chain code slopes of the image strips (frames), scanned from right to left by a sliding window, is used as feature vectors. Fuzzy c-means (FCM) clustering is used for generating a fuzzy code book. A separate HMM is trained by a modified Baum-Welch algorithm for each city name. A test image is recognized by finding the best match (likelihood) between the image and all of the HMM work models using a forward algorithm. Experimental results show the advantages of using an FVQ/HMM recognizer engine instead of conventional discrete HMMs.

Reference and non-reference video quality evaluation

Abstract: Devices and methods for determining image quality using full-reference and non-reference techniques. Full reference image quality may be determined prior to output of an image or video frame from an image sensor processor by temporarily retaining image data from the image sensor and comparing processed image data of the image to the retained, non-processed image data of the same image. Full reference image quality determination may be assisted by a heuristic-based fault indicator. Image quality may also be determined by a non-reference technique of matching the image to one of various scenarios that are associated with sets of heuristics and applying the heuristics of the particular scenario to the image. Instead of relying on a nominal frame rate, video timing quality may be determined by comparing the capture time interval between successive video frames to the presentation time interval of the same video frames.

Image processing apparatus and method for allowing the dynamic range of digital image data having a wide dynamic range to be compressed while preserving information of the digital image data

Abstract: An image processing apparatus and method is provided which makes it possible to reproduce all information of digital image data having a wide dynamic range. A high frequency component of luminance image data is removed by a digital filter. The luminance image data whose high frequency component is removed is divided into highlight image data and shadow image data. Level conversion processing is performed so that the luminance level of the blackest point of the highlight image and the luminance level of the whitest point of the shadow image coincide with each other. Luminance image data is generated by synthesizing the highlight image data, the shadow image data, and the high frequency component. A thermal head is driven on the basis of the luminance image data generated and chrominance image data, to print an image.

A Wavelet-PCA-Based Fingerprinting Scheme for Peer-to-Peer Video File Sharing

Abstract: In order to utilize peer-to-peer (P2P) networks in legal content distribution to benefit the legal content providers, copyright protection needs to be enhanced. In this paper, a fingerprint generation and embedding method is proposed for complex P2P file sharing networks. In this method, wavelet and principal component analysis (PCA) techniques are used for fingerprint generation. First, the wavelet technique obtains a low-frequency representation of the test image (or source file, which is assumed to be one I frame of a video with a DVD quality) and PCA finds the features of the representation. Then, a set of fingerprint matrices can be created based on a proposed algorithm. Finally, each matrix combines with the low-frequency representative to become a unique fingerprinted matrix. The fingerprinted matrix is not only much smaller than the original image in size but also contains the most important information. Without this information, the quality of the reconstructed image will be very poor. Thus, the fingerprinted file is more suitable for distribution in P2P networks, because, in the distribution stage, the uniquely fingerprinted matrix will only be dispensed by the source host and leave the rest for P2P networks to handle. On the other hand, among other frames of the same video which are not decomposed, some will be embedded with sharable fingerprints. The relationship between unique fingerprint and sharable fingerprint and the purpose of using it will be discussed in the paper. Our result indicates that the proposed fingerprint has shown strong robustness against common attacks such as Gaussian noise, median filter, and lossy compression.