scholarly journals Robust and Accurate Detection of Object Orientation and ID without Color Segmentation

10.5772/5141 ◽  
2007 ◽  
Author(s):  
Hironobu Fujiyoshi ◽  
Tomoyuki Nagahashi ◽  
Shoichi Shimizu
Keyword(s):  
Object Orientation ◽  
Color Segmentation ◽  
Accurate Detection

Animal Recognition and Eye Movements

2010 ◽  
Vol 57 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Toby J. Lloyd-Jones ◽  
Juergen Gehrke ◽  
Jason Lauder
Keyword(s):  
Eye Movements ◽  
Response Times ◽  
Real Life ◽  
Object Orientation ◽  
Decision Task ◽  
Line Drawings ◽  
Object Decision ◽  
Individual Features ◽  
And Performance

We assessed the importance of outline contour and individual features in mediating the recognition of animals by examining response times and eye movements in an animal-object decision task (i.e., deciding whether or not an object was an animal that may be encountered in real life). There were shorter latencies for animals as compared with nonanimals and performance was similar for shaded line drawings and silhouettes, suggesting that important information for recognition lies in the outline contour. The most salient information in the outline contour was around the head, followed by the lower torso and leg regions. We also observed effects of object orientation and argue that the usefulness of the head and lower torso/leg regions is consistent with a role for the object axis in recognition.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Precise Localization of 28 nm via Chain Resistive Defect Using EBAC and Nanoprobing

2012 ◽  
Author(s):  
P. Larré ◽  
H. Tupin ◽  
C. Charles ◽  
R.H. Newton ◽  
A. Reverdy
Keyword(s):  
Electron Beam ◽  
Failure Analysis ◽  
Test Structure ◽  
Precise Localization ◽  
Isolation Method ◽  
Accurate Detection ◽  
Conventional Methods ◽  
Technology Nodes ◽  
Tem Lamella

Abstract As technology nodes continue to shrink, resistive opens have become increasingly difficult to detect using conventional methods such as AVC and PVC. The failure isolation method, Electron Beam Absorbed Current (EBAC) Imaging has recently become the preferred method in failure analysis labs for fast and highly accurate detection of resistive opens and shorts on a number of structures. This paper presents a case study using a two nanoprobe EBAC technique on a 28nm node test structure. This technique pinpointed the fail and allowed direct TEM lamella.

scholarly journals A Novel Stacking Approach for Accurate Detection of Fake News

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 22626-22639
Author(s):  
Tao Jiang ◽  
Jian Ping Li ◽  
Amin Ul Haq ◽  
Abdus Saboor ◽  
Amjad Ali
Keyword(s):  
Fake News ◽  
Accurate Detection

Ultrasensitive detection of specific IgE based on nanomagnetic capture and separation with AuNP-anti-IgE nanobioprobe for signal amplification

2021 ◽  
Author(s):  
Mengda Cao ◽  
Yongxin Liu ◽  
Chen Lu ◽  
Miao Guo ◽  
Lin Li ◽  
...  
Keyword(s):  
Allergic Disease ◽  
Signal Amplification ◽  
Specific Ige ◽  
Ultrasensitive Detection ◽  
Accurate Detection ◽  
Personalized Diagnosis

The accurate detection of allergen specific IgE (sIgE) is fundamental in the diagnosis of allergic disease. The present commercial platforms fail to meet the need for personalized diagnosis, due to...

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