Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization

Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications. Although histogram equalization is widely used in image enhancement due to its simplicity and effectiveness, it changes the mean brightness of the enhanced image and introduces a high level of noise and distortion. To address these problems, this paper proposes image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization (FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of the original image, and then clip the histogram adaptively in order to prevent excessive image enhancement. Experiments on the Berkeley database and CVF-UGR-Image database show that FIMHE outperforms state-of-the-art histogram equalization based methods

Imaging Neural Activity in the Primary Somatosensory Cortex Using Thy1-GCaMP6s Transgenic Mice

The mammalian brain exhibits marked symmetry across the sagittal plane. However, detailed description of neural dynamics in symmetric brain regions in adult mammalian animals remains elusive. In this study, we describe an experimental procedure for measuring calcium dynamics through dual optical windows above bilateral primary somatosensory corticies (S1) in Thy1-GCaMP6s transgenic mice using 2-photon (2P) microscopy. This method enables recordings and quantifications of neural activity in bilateral mouse brain regions one at a time in the same experiment for a prolonged period in vivo. Key aspects of this method, which can be completed within an hour, include minimally invasive surgery procedures for creating dual optical windows, and the use of 2P imaging. Although we only demonstrate the technique in the S1 area, the method can be applied to other regions of the living brain facilitating the elucidation of structural and functional complexities of brain neural networks

Longitudinal Optogenetic Motor Mapping Revealed Structural and Functional Impairments and Enhanced Corticorubral Projection after Contusive Spinal Cord Injury in Mice

Current evaluation of impairment and repair after spinal cord injury (SCI) is largely dependent on behavioral assessment and histological analysis of injured tissue and pathways. Here, we evaluated whether transcranial optogenetic mapping of motor cortex could reflect longitudinal structural and functional damage and recovery after SCI. In Thy1-Channelrhodopsin2 transgenic mice, repeated motor mappings were made by recording optogenetically evoked electromyograms (EMGs) of a hindlimb at baseline and 1 day and 2, 4, and 6 weeks after mild, moderate, and severe spinal cord contusion. Injuries caused initial decreases in EMG amplitude, losses of motor map, and subsequent partial recoveries, all of which corresponded to injury severity. Reductions in map size were positively correlated with motor performance, as measured by Basso Mouse Scale, rota-rod, and grid walk tests, at different time points, as well as with lesion area at spinal cord epicenter at 6 weeks post-SCI. Retrograde tracing with Fluoro-Gold showed decreased numbers of cortico- and rubrospinal neurons, with the latter being negatively correlated with motor map size. Combined retro- and anterograde tracing and immunostaining revealed more neurons activated in red nucleus by cortical stimulation and enhanced corticorubral axons and synapses in red nucleus after SCI. Electrophysiological recordings showed lower threshold and higher amplitude of corticorubral synaptic response after SCI. We conclude that transcranial optogenetic motor mapping is sensitive and efficient for longitudinal evaluation of impairment and plasticity of SCI, and that spinal cord contusion induces stronger anatomical and functional corticorubral connection that may contribute to spontaneous recovery of motor function

Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis

This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multiview gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness