Uninformative Frame Detection in Colonoscopy Through Motion, Edge and Color Features

2016 ◽  
pp. 153-162 ◽  
Author(s):  
Mohammad Ali Armin ◽  
Girija Chetty ◽  
Fripp Jurgen ◽  
Hans De Visser ◽  
Cedric Dumas ◽  
...  
Keyword(s):  
Color Features

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

scholarly journals Model for identification of rice type using combination of shape and color features

2021 ◽  
Vol 1108 (1) ◽  
pp. 012038
Author(s):  
J Jumi ◽  
A Zaenuddin ◽  
T Mulyono
Keyword(s):  
Color Features

Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images

2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
Author(s):  
Christian Eckes ◽  
Jochen Triesch ◽  
Christoph von der Malsburg
Keyword(s):  
Graph Matching ◽  
Recognition Performance ◽  
Stereo Image ◽  
Matching Algorithm ◽  
Automatic Interpretation ◽  
Color Features ◽  
Occluded Objects ◽  
Elastic Graph Matching ◽  
Image Pairs ◽  
Object Models

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.

Segmentation of Kidneys from Computed Tomography Using 3D Fast GrowCut Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1145-1150 ◽  
Author(s):  
Gao Yuan Dai ◽  
Zhi Cheng Li ◽  
Jia Gu ◽  
Lei Wang ◽  
Xing Min Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Experimental Results ◽  
Volume Data ◽  
Great Role ◽  
Color Features ◽  
Monotonically Decreasing Function ◽  
Kidney Segmentation ◽  
Ct Volume

This paper proposes a fast GrowCut (FGC) algorithm and applies the new algorithm in three-dimensional (3D)kidney segmentation from computed tomography (CT) volume data. Users could mark the object of interest with different labels in CT slices.FGC propagates the labels using monotonically decreasing function and color features to derive an optimal cut for a given data in space. The color features play a great role in comparing with neighborhood cells. The experimental results clearly demonstrate the superiority of FGC in accuracy and speed.

Sign in / Sign up

Export Citation Format

Share Document