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Detecting spot-like objects of different sizes in images is needed in many applications. Multiple image scales must then be handled for reliable spot segmentation.
We define an original criterion based on the a contrario approach and the LoG scale-space framework to automatically select the meaningful scales.
We then design a coarse-to-fine multi-scale spot segmentation scheme involving
a locally adaptive thresholding across scales, to come up with the final map of segmented spots.

In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilize the structural

We propose a novel appearance-based gesture recognition algorithm using compressed domain signal processing tech- niques. Gesture features are extracted directly from the compressed measurements, which are the block averages and the coded linear combinations of the image sensor’s pixel values. We also improve both the computational efficiency and the memory requirement of the previous DTW-based K-NN gesture classifiers. Both simulation testing and hardware implementation strongly support the proposed algorithm.

Contextual information such as the co-occurrence of objects and the location of objects has played an important role in object detec- tion. We present candidate pruning and object rescoring methods that leverage contextual information and that can improve the state- of-the-art CNN-based object detection methods such as Fast R-CNN and Faster R-CNN. In our pruning method, we formulate candidate reduction as a Markov random field optimization problem. In our rescoring method, we employ a machine learning technique to recon- sider the detection scores of candidate windows.

We propose a full reference stereo video quality assessment
algorithm for assessing the perceptual quality of natural stereo
videos. We exploit the separable representation of motion
and binocular disparity in the visual cortex and develop a
four stage algorithm to measure the quality of a stereoscopic
video called FLOSIM3D. First, we compute the temporal features
by utilizing an existing 2D VQA metric which measures
the temporal annoyance based on patch level statistics such
as mean, variance and minimum eigen value and pools them

We propose a real-time plane detection method for projection-based Augmented Reality (AR) system in an unknown environment. While previous works usually designate space, the plane detection method automatically detects multiple planes based on the proposed constrained sampling strategy in RAndom SAmpleing Concensus (RANSAC). For each plane, an area for projection is selected for contents while considering occlusions by other objects.

In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise.Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this paper, we propose low light image enhancement based on two-step noise suppression. First, we perform noise aware contrast enhancement using noise level function (NLF).

First-person action recognition is a recent problem in computer vision, where an observer wears body cameras to understand and recognize actions from the captured video sequences. Technological advances have made it possible to offer small wearable cameras that can be attached onto bike helmets, belts, animal halters, among other accessories. Examples of potential applications include sports, security, healthcare, visual lifelogging, among others.