Alok Desai

An efficient feature descriptor based on synthetic basis functions and uniqueness matching strategy

SYBA is built on the basis of the compressed sensing theory.The descriptor is robust, simple and computationally efficient.Evaluated the descriptor performance statistically on BYU feature matching dataset. Feature matching is an important step for many computer vision applications. This paper introduces the development of a new feature descriptor, called SYnthetic BAsis (SYBA), for feature point description and matching. SYBA is built on the basis of the compressed sensing theory that uses synthetic basis functions to encode or reconstruct a signal. It is a compact and efficient binary descriptor that performs a number of similarity tests between a feature image region and a selected number of synthetic basis images and uses their similarity test results as the feature descriptors. SYBA is compared with four well-known binary descriptors using three benchmarking datasets as well as a newly created dataset that was designed specifically for a more thorough statistical T-test. SYBA is less computationally complex and produces better feature matching results than other binary descriptors. It is hardware-friendly and suitable for embedded vision applications. Display Omitted

Efficient Tree-Based Feature Descriptor and Matching Algorithm

This paper presents the development of a new feature descriptor derived from previous work on the basis sparse-coding inspired similarity descriptor that provides smaller descriptor size, simpler computations, faster matching speed, and higher accuracy. The TreeBASIS descriptor algorithm uses a binary vocabulary tree that is computed offline using basis sparse-coding inspired similarity dictionary images derived from sparse coding and a test set of feature region images. The resulting tree is stored in memory for online high-speed searching for feature matching. During the online matching stage, a feature region image is binary quantized and the resulting quantized vector is passed into the basis sparse-coding inspired similarity tree. A Hamming distance is computed between the feature region images and the effectively descriptive basis sparse-coding inspired similarity dictionary images at the current node to determine the branch taken. The path the feature region image takes is saved as the descriptor, ...

Stabilization and Control of Quad-Rotor Helicopter Using A Smartphone Device

In recent years, autonomous, micro-unmanned aerial vehicles (micro-UAVs), or more specifically hovering micro- UAVs, have proven suitable for many promising applications such as unknown environment exploration and search and rescue operations. The early versions of UAVs had no on-board control capabilities, and were difficult for manual control from a ground station. Many UAVs now are equipped with on-board control systems that reduce the amount of control required from the ground-station operator. However, the limitations on payload, power consumption and control without human interference remain the biggest challenges. This paper proposes to use a smartphone as the sole computational device to stabilize and control a quad-rotor. The goal is to use the readily available sensors in a smartphone such as the GPS, the accelerometer, the rate-gyros, and the camera to support vision-related tasks such as flight stabilization, estimation of the height above ground, target tracking, obstacle detection, and surveillance. We use a quad-rotor platform that has been built in the Robotic Vision Lab at Brigham Young University for our development and experiments. An Android smartphone is connected through the USB port to an external hardware that has a microprocessor and circuitries to generate pulse-width modulation signals to control the brushless servomotors on the quad-rotor. The high-resolution camera on the smartphone is used to detect and track features to maintain a desired altitude level. The vision algorithms implemented include template matching, Harris feature detector, RANSAC similarity-constrained homography, and color segmentation. Other sensors are used to control yaw, pitch, and roll of the quad-rotor. This smartphone-based system is able to stabilize and control micro-UAVs and is ideal for micro-UAVs that have size, weight, and power limitations.

Matching Affine Features with the SYBA Feature Descriptor

Many vision-based applications require a robust feature descriptor that works well with image deformations such as compression, illumination, and blurring. It remains a challenge for a feature descriptor to work well with image deformation caused by viewpoint change. This paper introduces, first, a new binary feature descriptor called SYnthetic BAsis (SYBA) for feature point description and matching, and second, a method for removing non-affine features from the initial feature list to further improve the feature matching accuracy. This new approach has been tested on the Oxford dataset and a newly created dataset by comparing the feature matching accuracy using only affine features with the accuracy of using both affine and non-affine features. A statistical T-test was performed on the newly created dataset to demonstrate the advantages of using only affine feature points for matching. SYBA is less computationally complex than other feature descriptors and gives better feature matching results using affine features.

Using affine features for an efficient binary feature descriptor

A feature descriptor that is robust to a number of image deformations is a basic requirement for vision based applications. Most feature descriptors work well in image deformations such as compression artifacts, illumination changes, and blurring. To develop a feature descriptor that works well apart from these image deformations like transformations caused by long baseline is a challenging task. This paper introduces a compact and efficient binary feature descriptor called PRObabilistic (PRO). A method for removing non-affine features from the initial feature list is developed, which results in further improved performance with the PRO descriptor when dealing with many deformations including long baseline between images. Feature matching accuracy using only affine features is compared with accuracy using both affine and non-affine features on benchmark datasets to demonstrate the advantages of using affine feature point for PRO descriptor.

Using Accurate Feature Matching for Unmanned Aerial Vehicle Ground Object Tracking

Tracking moving objects with a moving camera is a challenging task. For unmanned aerial vehicle applications, targets of interest such as human and vehicles often change their location from image frame to frame. This paper presents an object tracking method based on accurate feature description and matching, using the SYnthetic BAsis descriptor, to determine a homography between the previous frame and the current frame. Using this homography, the previous frame can be transformed and registered to the current frame to find the absolute difference and locate the objects. Once the objects of interest are located, the Kalman filter is then used for tracking their movement. This proposed method is evaluated with three video sequences under image deformation: illumination change, blurring and camera movement (i.e. viewpoint change). These video sequences are taken from unmanned aerial vehicles (UAVs) for tracking stationary and moving objects with a moving camera.

Automatic Facial Expression Recognition Using Evolution-Constructed Features

One of the common ways of human showing emotion is through the change in facial expression. In this paper, we propose a new method for emotion detection by analyzing facial expression images. Facial expression information is analyzed by using a new feature construction method called Evolution-COnstructed (ECO) Features. The proposed algorithm is able to automatically recognize seven basic emotions that include Anger, Contempt, Disgust, Fear, Happiness, Sadness and Surprise. The test results on the Cohn- Kanade dataset show that the proposed algorithm has a very high classification accuracy.

TreeBASIS feature descriptor and its hardware implementation

This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature region image is binary quantized and the resulting quantized vector is passed into the BASIS vocabulary tree. A Hamming distance is then computed between the feature region image and the effectively descriptive basis dictionary image at a node to determine the branch taken and the path the feature region image takes is saved as a descriptor. The TreeBASIS feature descriptor is an excellent candidate for hardware implementation because of its reduced descriptor size and the fact that descriptors can be created and features matched without the use of floating point operations. The TreeBASIS descriptor is more computationally and space efficient than other descriptors such as BASIS, SIFT, and SURF. Moreover, it can be computed entirely in hardware without the support of a CPU for additional software-based computations. Experimental results and a hardware implementation show that the TreeBASIS descriptor compares well with other descriptors for frame-to-frame homography computation while requiring fewer hardware resources.

Determine Absolute Soccer Ball Location in Broadcast Video Using SYBA Descriptor

This paper presents research work on the detection, tracking, and localization of the soccer ball in a broadcast soccer video and maps the ball locations to the global coordinate system of the soccer field. Because of the lack of reference points in these frames, the calculation of the global coordinates of the ball remains a very challenging task. This paper proposes to use an object-based algorithm and Kalman filter to detect and track the ball in such videos. Once the ball is located, frames are registered to static soccer field, and the absolute ball location is found in the field. The existing feature matching algorithms do not work well for frame registration, especially when involving lighting variations and large camera pan-tile-zoom change. To overcome this challenge, a new feature descriptor and matching algorithm that is robust to these deformations is developed and presented in this paper. Experimental results show the proposed algorithm is very effective and accurate.

Using probabilistic model as feature descriptor on a smartphone device for autonomous navigation of unmanned ground vehicles

There has been significant research on the development of feature descriptors in the past few years. Most of them do not emphasize real-time applications. This paper presents the development of an affine invariant feature descriptor for low resource applications such as UAV and UGV that are equipped with an embedded system with a small microprocessor, a field programmable gate array (FPGA), or a smart phone device. UAV and UGV have proven suitable for many promising applications such as unknown environment exploration, search and rescue operations. These applications required on board image processing for obstacle detection, avoidance and navigation. All these real-time vision applications require a camera to grab images and match features using a feature descriptor. A good feature descriptor will uniquely describe a feature point thus allowing it to be correctly identified and matched with its corresponding feature point in another image. A few feature description algorithms are available for a resource limited system. They either require too much of the device’s resource or too much simplification on the algorithm, which results in reduction in performance. This research is aimed at meeting the needs of these systems without sacrificing accuracy. This paper introduces a new feature descriptor called PRObabilistic model (PRO) for UGV navigation applications. It is a compact and efficient binary descriptor that is hardware-friendly and easy for implementation.