This paper presents another outlook on image description, classification and retrieval. Some popular image description methods are Histogram of Oriented Gradients (HoG), Speed Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT). While SURF and SIFT both use ”interest points” to describe an image, HoG uses all of the points in the image. One of the goals of this paper is to improve HoG by creating a feature vector containing more information about the image. The proposed description method is called the Histogram of Second order Oriented Gradients (HSoG) and it was shown to perform better than HoG using a dataset comprising of airplanes, cars and motorbikes by supervised learning. The second goal is to tackle image clustering for aid in unsupervised learning and this paper explores a method called Localized Clustering with a comparison to K-Means. The localized clustering approach does not require the number of clusters as an input but it does return what it determines the number of clusters should be. Finally, The retrieval process presented involves training a linear SVM with known labels (supervised) to evaluate the effectiveness of HoG vs HSoG and HSoG out performs HoG.
KEYWORDS: Visualization, Eye, Optical tracking, Clinical research, Data processing, Target acquisition, RGB color model, Data modeling, Brain, Data acquisition
We describe here the design and implementation of a software module that provides both auditory and visual feedback of the eye position measured by a commercially available eye tracking system. The present audio-visual feedback module (AVFM) serves as an extension to the Arrington Research ViewPoint EyeTracker, but it can be easily modified for use with other similar systems. Two modes of audio feedback and one mode of visual feedback are provided in reference to a circular area-of-interest (AOI). Auditory feedback can be either a click tone emitted when the user’s gaze point enters or leaves the AOI, or a sinusoidal waveform with frequency inversely proportional to the distance from the gaze point to the center of the AOI. Visual feedback is in the form of a small circular light patch that is presented whenever the gaze-point is within the AOI. The AVFM processes data that are sent to a dynamic-link library by the EyeTracker. The AVFM’s multithreaded implementation also allows real-time data collection (1 kHz sampling rate) and graphics processing that allow display of the current/past gaze-points as well as the AOI. The feedback provided by the AVFM described here has applications in military target acquisition and personnel training, as well as in visual experimentation, clinical research, marketing research, and sports training.
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