Model Simplification of Deep Random Forest for Real-Time Applications of Various Sensor Data.

The deep random forest (DRF) has recently gained new attention in deep learning because it has a high performance similar to that of a deep neural network (DNN) and does not rely on a backpropagation. However, it connects a large number of decision trees to multiple layers, thereby making analysis difficult. This paper proposes a new method for simplifying a black-box model of a DRF using a proposed rule elimination.

Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network.

Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easily implemented in various real-time applications. Moreover, the special equipment required by hardware-based approaches using 3D sensors is expensive.

Detecting humans using luminance saliency in thermal images.

This Letter introduces an efficient human detection method in thermal images, using a center-symmetric local binary pattern (CS-LBP) with a luminance saliency map and a random forest (RF) classifier scheme. After detecting a candidate human region, we crop only the head and shoulder region, which has a higher thermal spectrum than the legs or trunk. The CS-LBP feature is then extracted from the luminance saliency map of a hotspot and applied to the RF classifier, which is an ensemble of randomized decision trees.

Image resizing using saliency strength map and seam carving for white blood cell analysis.

Background: A new image-resizing method using seam carving and a Saliency Strength Map (SSM) is proposed to preserve important contents, such as white blood cells included in blood cell images.

Methods: To apply seam carving to cell images, a SSM is initially generated using a visual attention model and the structural properties of white blood cells are then used to create an energy map for seam carving. As a result, the energy map maximizes the energies of the white blood cells, while minimizing the energies of the red blood cells and background.

Microscopic cell nuclei segmentation based on adaptive attention window.

This paper presents an adaptive attention window (AAW)-based microscopic cell nuclei segmentation method. For semantic AAW detection, a luminance map is used to create an initial attention window, which is then reduced close to the size of the real region of interest (ROI) using a quad-tree. The purpose of the AAW is to facilitate background removal and reduce the ROI segmentation processing time.