scholarly journals OpenCV Image Processing for AI Pet Robot

2021 ◽  
Vol 03 (01) ◽  
pp. 65-82
Author(s):  
Abhishek Ghoshal ◽  
◽  
Aditya Aspat ◽  
Elton Lemos ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Science ◽  
Emotion Recognition ◽  
Face Tracking ◽  
The Face

The Artificial Intelligence (AI) Pet Robot is a culmination of multiple fields of computer science. This paper showcases the capabilities of our robot. Most of the functionalities stem from image processing made available through OpenCV. The functions of the robot discussed in this paper are face tracking, emotion recognition and a colour-based follow routine. Face tracking allows the robot to keep the face of the user constantly in the frame to allow capturing of facial data. Using this data, emotion recognition achieved an accuracy of 66% on the FER-2013 dataset. The colour-based follow routine enables the robot to follow the user as they walk based on the presence of a specific colour.

scholarly journals Stone, Paper, Scissors Mini-Game for AI Pet Robot

2021 ◽  
Vol 03 (01) ◽  
pp. 55-64
Author(s):  
Aditya Aspat ◽  
◽  
Elton Lemos ◽  
Abhishek Ghoshal ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Emotion Recognition ◽  
Background Subtraction ◽  
Convolutional Network

The Artificial Intelligence (AI) Pet Robot is a combination of various fields of computer science. This paper showcases the various functionalities of our AI Pet. Most of the functionalities showcased use the immage processing modules made available through OpenCV. The pet robot has various features such as emotion recognition, follow routine, mini-game etc. This paper discusses the mini-game aspect of the robot. The game has been developed by using VGG16 convolutional network for identification of the action performed by the user. To improve the accuracy we have made use of background subtraction which gives removes all the unwanted objects from the background and gives a simple cutout of the users hand.

scholarly journals Multimodal verge for scale and pose variant real time face tracking and recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 665
Author(s):  
Ramkumar Govindaraj ◽  
E. Logashanmugam
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Real Time ◽  
Research Field ◽  
Face Tracking ◽  
Low Rank ◽  
Facial Features ◽  
The Face ◽  
Low Rank Representation ◽  
Tracking And Recognition

In recent times face tracking and face recognition have turned out to be increasingly dynamic research field in image processing. This work proposed the framework DEtecting Contiguous Outliers in the LOw-rank Representation for face tracking, in this algorithm the background is assessed by a low-rank network and foreground articles can be distinguished as anomalies. This is suitable for non-rigid foreground motion and moving camera. The face of a foreground person is caught from the frame and then it is contrasted and the speculated pictures stored in the dataset. Here we used Viola-Jones algorithm for face recognition. This approach outperforms the traditional algorithms on multimodal video methodologies and it works adequately on extensive variety of security and surveillance purposes. Results on the continuous demonstrate that the proposed calculation can correctly obtain facial features points. The algorithm is relegate on the continuous camera input and under ongoing ecological conditions.

Data Analyzing via Probabilistic Modeling

2021 ◽  
pp. 25-51
Author(s):  
Dariusz Jacek Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing, and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. An important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation are possible using a new method of Hurwitz-Radon matrices.

scholarly journals Fuzzy Logic Using in Images Retrieval Depending on Their Features

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Faiez Musa Lahmood Alrufaye ◽  
Mohammed Muanis I. Al-Sagheer ◽  
Marwah Thamer Ali
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Image Recognition ◽  
Correct Result ◽  
Image Information ◽  
Fuzzy Logic Algorithm ◽  
Image Characteristics ◽  
Content Information

Image processing has become one of the most important branches of computer science, especially after entering into several areas of life such as medicine, engineering and various sciences. In our current research, we have developed a system of image recognition based on image characteristics and some content information using the most important artificial intelligence algorithms, a fuzzy logic algorithm, to obtain complete image information using small values ranging from 0 to 1. The program was executed on a set of standard database called the WANG database. It holds the contents of 1000 images from the Corel stock photo database, in JPEG format. The system was evaluated using the recall method. This method calculates the proportion of correct results identified by the system as correct results with correct result identified by the classic system.

Object Recognition via Contour Points Reconstruction Using Hurwitz - Radon Matrices

2013 ◽  
pp. 998-1018
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. Important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation is possible using new method of Hurwitz - Radon Matrices.

Object Recognition via Contour Points Reconstruction Using Hurwitz - Radon Matrices

2011 ◽  
pp. 87-107 ◽  
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Computer Science ◽  
Classical Problem ◽  
Contour Reconstruction ◽  
Curve Interpolation ◽  
Characteristic Features ◽  
Unknown Object

Object recognition is one of the topics of artificial intelligence, computer vision, image processing and machine vision. The classical problem in these areas of computer science is that of determining object via characteristic features. Important feature of the object is its contour. Accurate reconstruction of contour points leads to possibility to compare the unknown object with models of specified objects. The key information about the object is the set of contour points which are treated as interpolation nodes. Classical interpolations (Lagrange or Newton polynomials) are useless for precise reconstruction of the contour. The chapter is dealing with proposed method of contour reconstruction via curves interpolation. First stage consists in computing the contour points of the object to be recognized. Then one can compare models of known objects, given by the sets of contour points, with coordinates of interpolated points of unknown object. Contour points reconstruction and curve interpolation is possible using new method of Hurwitz - Radon Matrices.

Emotion Recognition and Drowsiness Detection using Digital Image Processing and Python

2021 ◽  
pp. 1037-1043
Author(s):  
Pramoda R ◽  
Arun P S ◽  
B S Athul ◽  
Bharath B ◽  
B Naveen Reddy
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Emotion Recognition ◽  
Digital Image Processing ◽  
Digital Image ◽  
Automobile Accidents ◽  
Processing Technologies ◽  
Drowsiness Detection

We suggest a system that can detect a person's emotions as well as their level of sleepiness. The majority of our work is devoted to extracting information from the frontal face. The article goal is to create a product that is both reasonable and efficient in its operation. The system was created in Python using artificial intelligence and digital image processing technologies. Identifying eye blinking is essential in some situations, such as in the prevention of automobile accidents or the monitoring of safety vigilance.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

Emotion Recognition of Facial Expressions Presented in Profile

2021 ◽  
pp. 003329412110184
Author(s):  
Paola Surcinelli ◽  
Federica Andrei ◽  
Ornella Montebarocci ◽  
Silvana Grandi
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Response Times ◽  
Recognition Accuracy ◽  
Recognition Of Facial Expressions ◽  
Profile View ◽  
The Face ◽  
Within Subjects ◽  
Face Stimuli ◽  
Affective Information

Aim of the research The literature on emotion recognition from facial expressions shows significant differences in recognition ability depending on the proposed stimulus. Indeed, affective information is not distributed uniformly in the face and recent studies showed the importance of the mouth and the eye regions for a correct recognition. However, previous studies used mainly facial expressions presented frontally and studies which used facial expressions in profile view used a between-subjects design or children faces as stimuli. The present research aims to investigate differences in emotion recognition between faces presented in frontal and in profile views by using a within subjects experimental design. Method The sample comprised 132 Italian university students (88 female, Mage = 24.27 years, SD = 5.89). Face stimuli displayed both frontally and in profile were selected from the KDEF set. Two emotion-specific recognition accuracy scores, viz., frontal and in profile, were computed from the average of correct responses for each emotional expression. In addition, viewing times and response times (RT) were registered. Results Frontally presented facial expressions of fear, anger, and sadness were significantly better recognized than facial expressions of the same emotions in profile while no differences were found in the recognition of the other emotions. Longer viewing times were also found when faces expressing fear and anger were presented in profile. In the present study, an impairment in recognition accuracy was observed only for those emotions which rely mostly on the eye regions.

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