Hyperspectral Band Selection via Band Grouping and Adaptive Multi-Graph Constraint

Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining. This paper presents a novel unsupervised HSI band selection method via band grouping and adaptive multi-graph constraint. A band grouping strategy that assigns each group different weights to construct a global similarity matrix is applied to address the problem of overlooking strong correlations among adjacent bands. Different from previous studies that are limited to fixed graph constraints, we adjust the weight of the local similarity matrix dynamically to construct a global similarity matrix. By partitioning the HSI cube into several groups, the model is built with a combination of significance ranking and band selection. After establishing the model, we addressed the optimization problem by an iterative algorithm, which updates the global similarity matrix, its corresponding reconstruction weights matrix, the projection, and the pseudo-label matrix to ameliorate each of them synergistically. Extensive experimental results indicate our method outperforms the other five state-of-the-art band selection methods in the publicly available datasets

A novel rat head gaze determination system based on optomotor responses

<div><p>The optomotor response of animals is commonly used to measure their visual performance, e.g., rats of different genetically altered strains or various drug tests. With the presentation of stimuli using computer screens or projectors, the common idea focuses on measuring the eye movement or head and/or body movement to characterize changes of the head gaze. However, traditional methods rely on either the invasive fixation of animals, or the judgment of a human observer who reports the stimulus-tracking movements. In this paper, we propose a novel head gaze determination system to automatically track the head movement of rats without artificial markers. The experiments were done to demonstrate the process of optimizing parameters in image processing. As a result, the head angle curve of the proposed method is consistent with that of ground-truth data annotated manually according to predefined rules. Hence, the proposed method provides a simple, convenient, and objective solution to automatically generate the head gaze orientations from massive amounts of recorded data for further visual performance analysis.</p></div

An example of sinusoidal grating pattern: Purple and black.

<p>Gratings are designed to move clockwise, anti-clockwise, or stay stationary.</p

Comparison of pixel intensity value distribution on the entire image and ROI.

<p>A: Histogram of the entire image. B: Histogram of the ROI. Since all the images are captured under similar light conditions, this comparison is based on a single frame as an example.</p

An example of the head gaze determination results.

<p>The red line is the extracted body contour within the ROI. The blue arrow is the extracted head vector with the aid of four supporting points: two symmetrical points on the contour (in blue), their mid-point (in green) and the nose point (in green). All the points, lines, and text in this example image are automatically generated by the implementation program of the proposed method.</p

Determination results of head angle between proposed method and ground truth.

<p>This experiment operates on a dataset with ground truth by manual annotation. It contains 1500 captured images with head rotations over three dimensions of pitch, yaw, and roll. The red curve is the optimal determination results with our proposed method, with MSE of 6.73. The blue curve is the ground truth by manual annotation.</p

Curvatures and normals of the points on the contour.

<p>Step length of neighboring points: 16.</p

Overview of the apparatus to display the stimulus and record the response behavior.

<p>A projector connected to a laptop PC is utilized to present the stimulus on the screen. However, the projector and laptop are omitted in this figure to focus on the environment of testing subject. A: Top view. B: Side view.</p