Image Description Using a Multiplier-Less Operator

Cataloged from PDF version of article.A fast algorithm for image classification based on a computationally efficient operator forming a semigroup on real numbers is developed. The new operator does not require any multiplications. The co-difference matrix based on the new operator is defined and an image descriptor using the co-difference matrix is developed. In the proposed method, the multiplication operation of the well-known covariance method is replaced by the new operator. The proposed method is experimentally compared with the regular covariance matrix method. The proposed descriptor performs as well as the the regular covariance method without performing any multiplications. Texture recognition and licence plate identification examples are presented

Voronoi-Based Compact Image Descriptors: Efficient Region-of-Interest Retrieval With VLAD and Deep-Learning-Based Descriptors

We investigate the problem of image retrieval based on visual queries when the latter comprise arbitrary regionsof- interest (ROI) rather than entire images. Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoibased spatial partitioning of each dataset image. The proposed multi-level Voronoi-based encoding uses a spatial hierarchical K-means over interest-point locations, and computes a contentbased descriptor over each cell. In order to reduce the matching complexity with minimal or no sacrifice in retrieval performance: (i) we utilize the tree structure of the spatial hierarchical Kmeans to perform a top-to-bottom pruning for local similarity maxima; (ii) we propose a new image similarity score that combines relevant information from all partition levels into a single measure for similarity; (iii) we combine our proposal with a novel and efficient approach for optimal bit allocation within quantized descriptor representations. By deriving both a Voronoi-based VLAD descriptor (termed as Fast-VVLAD) and a Voronoi-based deep convolutional neural network (CNN) descriptor (termed as Fast-VDCNN), we demonstrate that our Voronoi-based framework is agnostic to the descriptor basis, and can easily be slotted into existing frameworks. Via a range of ROI queries in two standard datasets, it is shown that the Voronoibased descriptors achieve comparable or higher mean Average Precision against conventional grid-based spatial search, while offering more than two-fold reduction in complexity. Finally, beyond ROI queries, we show that Voronoi partitioning improves the geometric invariance of compact CNN descriptors, thereby resulting in competitive performance to the current state-of-theart on whole image retrieval

Identity verification using computer vision for automatic garage door opening

We present a novel system for automatic identification of vehicles as part of an intelligent access control system for a garage entrance. Using a camera in the door, cars are detected and matched to the database of authenticated cars. Once a car is detected, License Plate Recognition (LPR) is applied using character detection and recognition. The found license plate number is matched with the database of authenticated plates. If the car is allowed access, the door will open automatically. The recognition of both cars and characters (LPR) is performed using state-ofthe- art shape descriptors and a linear classifier. Experiments have revealed that 90% of all cars are correctly authenticated from a single image only. Analysis of the computational complexity shows that an embedded implementation allows user authentication within approximately 300ms, which is well within the application constraints

Evaluation of Robust Covariance Estimation for Object Detection

This work presents an initial approach to the evaluation of robust covariance estimation for object detection (localization) using the “region covariance” technique from the literature. The covariance estimation is performed using the Comedian, Kendall, Spearman and Ledoit and Wolf robust approaches for covariance, and the procedure was also compared using two different matrix norms for estimating dissimilarity. The performance was measured quantitatively using linear regression and Pareto boundaries, yielding the Ledoit and Wolf estimation with best overall performance in object detection in normal and noisy images

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Vehicle make and model recognition using bag of expressions

This article belongs to the Section Intelligent SensorsVehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.Muhammad Haroon Yousaf received funding from the Higher Education Commission, Pakistan for Swarm Robotics Lab under the National Centre for Robotics and Automation (NCRA). The authors also acknowledge support from the Directorate of ASR& TD, University of Engineering and Technology Taxila, Pakistan