Mouth Movement Recognition Using Template Matching and its Implementation in an Intelligent Room

2012 ◽  
Vol 24 (2) ◽  
pp. 311-319
Author(s):  
Kiyoshi Takita ◽  
◽  
Takeshi Nagayasu ◽  
Hidetsugu Asano ◽  
Kenji Terabayashi ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Similarity Measure ◽  
Template Matching ◽  
Target Object ◽  
Facial Region ◽  
Mouth Region ◽  
Home Appliances ◽  
Movement Recognition ◽  
Template Image ◽  
Mouth Movement

This paper proposes a method of recognizing movements of the mouth from images and implements the method in an intelligent room. The proposed method uses template matching and recognizes mouth movements for the purpose of indicating a target object in an intelligent room. First, the operator’s face is detected. Then, the mouth region is extracted from the facial region using the result of template matching with a template image of the lips. Dynamic Programming (DP) matching is applied to a similarity measure that is obtained by template matching. The effectiveness of the proposed method is evaluated through experiments to recognize several names of common home appliances and operations.

scholarly journals Feasibility Study of Textureless Object Detection and Pose Estimation Based on a Model with 3D Edgels and Surfaces

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Kimitoshi Yamazaki ◽  
Kiyohiro Sogen ◽  
Takashi Yamamoto ◽  
Masayuki Inaba
Keyword(s):  
Object Detection ◽  
Feasibility Study ◽  
Pose Estimation ◽  
Template Matching ◽  
Input Data ◽  
Three Dimensional ◽  
Target Object ◽  
Textural Features ◽  
Two Dimensional ◽  
Home Appliances

Abstract This paper describes a method for the detection of textureless objects. Our target objects include furniture and home appliances, which have no rich textural features or characteristic shapes. Focusing on the ease of application, we define a model that represents objects in terms of three-dimensional edgels and surfaces. Object detection is performed by superimposing input data on the model. A two-stage algorithm is applied to bring out object poses. Surfaces are used to extract candidates fromthe input data, and edgels are then used to identify the pose of a target object using two-dimensional template matching. Experiments using four real furniture and home appliances were performed to show the feasibility of the proposed method.We suggest the possible applicability in occlusion and clutter conditions.

A SIFT Feature-Based Template Matching Method for Detecting and Counting Objects in Life Space

2010 ◽  
Vol 36 ◽  
pp. 413-421 ◽  
Author(s):  
Hideaki Kawano ◽  
Hideaki Orii ◽  
Katsuaki Shiraishi ◽  
Hiroshi Maeda
Keyword(s):  
Template Matching ◽  
Autonomous Robots ◽  
Target Object ◽  
Main Process ◽  
Spatial Transformation ◽  
Scale Invariant ◽  
Everyday Objects ◽  
Counting Task ◽  
Feature Based ◽  
Scale Invariant Feature

Autonomous robots are at advanced stage in various fields, and they are expected to autonomously work at the scenes of nursing care or medical care in the near future. In this paper, we focus on object counting task by images. Since the number of objects is not a mere physical quantity, it is difficult for conventional phisical sensors to measure such quantity and an intelligent sensing with higher-order recognition is required to accomplish such counting task. It is often that we count the number of objects in various situations. In the case of several objects, we can recognize the number at a glance. On the other hand, in the case of a dozen of objects, the task to count the number might become troublesome. Thus, simple and easy way to enumerate the objects automatically has been expected. In this study, we propose a method to recognize the number of objects by image. In general, the target object to count varies according to user's request. In order to accept the user's various requests, the region belonging to the desired object in the image is selected as a template. Main process of the proposed method is to search and count regions which resembles the template. To achieve robustness against spatial transformation, such as translation, rotation, and scaling, scale-invariant feature transform (SIFT) is employed as a feature. To show the effectiveness, the proposed method is applied to few images containing everyday objects, e.g., binders, cans etc.

scholarly journals An Improved Algorithm for Detection and Pose Estimation of Texture-Less Objects

2021 ◽  
Vol 25 (2) ◽  
pp. 204-212
Author(s):  
Jian Peng ◽  
Ya Su ◽  
◽  
Keyword(s):  
Pose Estimation ◽  
Template Matching ◽  
Target Object ◽  
Test Image ◽  
Image Region ◽  
Multi Scale ◽  
Training Stage ◽  
Sensitivity Problem ◽  
Depth Sensitivity ◽  
Improved Algorithm

This paper introduces an improved algorithm for texture-less object detection and pose estimation in industrial scenes. In the template training stage, a multi-scale template training method is proposed to improve the sensitivity of LineMOD to template depth. When this method performs template matching, the test image is first divided into several regions, and then training templates with similar depth are selected according to the depth of each test image region. In this way, without traversing all the templates, the depth of the template used by the algorithm during template matching is kept close to the depth of the target object, which improves the speed of the algorithm while ensuring that the accuracy of recognition will not decrease. In addition, this paper also proposes a method called coarse positioning of objects. The method avoids a lot of useless matching operations, and further improves the speed of the algorithm. The experimental results show that the improved LineMOD algorithm in this paper can effectively solve the algorithm’s template depth sensitivity problem.

Similarity-transform invariant similarity measure for robust template matching

2018 ◽  
Author(s):  
Shengyi Chen ◽  
Haibo Liu ◽  
Mengna Jia ◽  
Cong Sun ◽  
Xiangyi Sun ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Template Matching ◽  
Similarity Transform

scholarly journals Research on track fastener positioning method based on local unidirectional template matching

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Liang Li ◽  
Zhaomin Lv ◽  
Xingjie Chen ◽  
Yijin Qiu ◽  
Liming Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Template Matching ◽  
Surrounding Area ◽  
Vertical Projection ◽  
Ballastless Track ◽  
Complex Background ◽  
Template Image ◽  
Ballasted Track ◽  
Positioning Method ◽  
Segmentation Threshold

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

scholarly journals Neural Signatures of Learning Novel Object–Scene Associations

2020 ◽  
Vol 32 (5) ◽  
pp. 783-803
Author(s):  
Cybelle M. Smith ◽  
Kara D. Federmeier
Keyword(s):  
Template Matching ◽  
Paired Associate ◽  
Memory Task ◽  
Target Object ◽  
Clinical Implications ◽  
Associative Memories ◽  
Natural Scene ◽  
Visual Distance ◽  
Novel Objects ◽  
Scene Object

Objects are perceived within rich visual contexts, and statistical associations may be exploited to facilitate their rapid recognition. Recent work using natural scene–object associations suggests that scenes can prime the visual form of associated objects, but it remains unknown whether this relies on an extended learning process. We asked participants to learn categorically structured associations between novel objects and scenes in a paired associate memory task while ERPs were recorded. In the test phase, scenes were first presented (2500 msec), followed by objects that matched or mismatched the scene; degree of contextual mismatch was manipulated along visual and categorical dimensions. Matching objects elicited a reduced N300 response, suggesting visuostructural priming based on recently formed associations. Amplitude of an extended positivity (onset ∼200 msec) was sensitive to visual distance between the presented object and the contextually associated target object, most likely indexing visual template matching. Results suggest recent associative memories may be rapidly recruited to facilitate object recognition in a top–down fashion, with clinical implications for populations with impairments in hippocampal-dependent memory and executive function.

A Real-Time NCC-Based Template Matching on Modern CPUs

2015 ◽  
Vol 764-765 ◽  
pp. 1288-1292
Author(s):  
Chin Sheng Chen ◽  
Chien Liang Huang ◽  
Chun Wei Yeh
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Color Image ◽  
Target Object ◽  
Similarity Coefficient ◽  
Color Images ◽  
Search Process ◽  
Optimal Method ◽  
Computation Efficiency ◽  
Time Critical

This paper proposes an optimal method for NCC-based template matching on modern CPUs for time-critical applications. In order to achieve the superior computation efficiency, the brand-and-bound (BB) scheme and the streaming SIMD extensions 2 (SSE2) instructions are employed to quickly find out the target object with rotation, translation and scaling in monochrome or color image. And we show how to reject unpromising image location very quickly using BB scheme in search process. Furthermore, an efficient implementation for similarity coefficient calculation is also pointed out by using the integration SSE2 instructions. Finally, the results show that the proposed method is very powerful when dealing with the NCC-based template matching in monochrome and color images.

Research of an Improved Snail Image Recognition Method Based on Grayscale Template Matching

2013 ◽  
Vol 433-435 ◽  
pp. 700-704
Author(s):  
Yin E Zhang
Keyword(s):  
Template Matching ◽  
Source Image ◽  
Complex Environment ◽  
Recognition Method ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Template Image ◽  
Traditional Algorithm ◽  
Template Selection

As the lack in the accuracy and speed of the template matching algorithm for the snail image in the complex environment, the snail source image and the template image have the appropriate scaling in order to improve their sizes in the traditional algorithm. The new algorithm avoids the very big and accurate characteristics about the snail images through shrinking the source images down. The grayscale template matching method is put forward based on the traditional template selection set to prevent that the error caused by human factors on the selected template, the redundancy between the templates is removed in a large extent, further the accuracy of the matching is improved, and the matching time is reduced greatly in the case of matching accuracy guarantee.

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