Image matching with fuzzy moment descriptors

Biswas, Bijita; Konar, Amit; Mukherjee, Aritra

doi:10.1016/s0952-1976(00)00058-0

Cited by 19 publications

(6 citation statements)

References 10 publications

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“…There exists work on feedback models of emotional dynamics, cited in [6], but the approach undertaken here is new and unknown in the domain of emotional intelligence. Stimulated brain imaging, including Functional Magnetic Resonance Imaging (fMRI) [8], and Positron Emission Tomography undertaken in [10,11] supports the response of the mathematical model of emotional dynamics presented in this paper [9].…”

Section: Proposed Model For Chaotic Emotional Dynamicssupporting

confidence: 54%

Chaos and Stability in the Dynamic Behavior of the Multi-emotion Interactions

Ghosh

Chakraborty²,

Konar

et al. 2008

2008 16th International Conference on Advanced Computing and Communications

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The paper proposes a model to represent the interactive dynamics of competitive/co-operative emotions, aroused by audio-visual stimulus. A study of parameter variations of the dynamics is undertaken to examine the scope of chaos, and stability. The conditions for stability and chaos are derived. The main challenge of the paper lies in prediction of chaos and stability of emotional dynamics from the manifestation of emotion in facial expression, aroused by external audio-visual stimulus. Such prediction may help in early detection of trauma and epilepsy.

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Section: Proposed Model For Chaotic Emotional Dynamicssupporting

confidence: 54%

Chaos and Stability in the Dynamic Behavior of the Multi-emotion Interactions

Ghosh

Chakraborty²,

Konar

et al. 2008

2008 16th International Conference on Advanced Computing and Communications

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Hausdorff distance [3], adopted for the comparison of two images, is to calculate the maximal interpixel distance between their corresponding edge point sets. only one, this denotes that to any point of the real time image feature point set, there is not more than one point in the reference image feature point set whose distance to the real time image feature point set is equal to the Hausdorff distance between the two corresponding image feature points set.…”

Section: The Hausdorff Distancementioning

confidence: 99%

Visual Perception based Motion Planning of Mobile Robot using Road Sign

KDas¹,

Mandhata²,

Behera³

et al. 2012

IJCA

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In this paper a new method of road map based navigation is proposed. A vision based motion planning of a mobile robot is implemented in a predefined road map. Road map is build with the left and right lane at the junction constructed with 90 degree with respect to the main lane. In our realization the robot moves towards a junction and at each junction takes a photograph of the road sign map and an image matching algorithm is performed at the host machine to compare the captured image with the map stored in the memory and decide the next course of action.

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“…The problem of face recognition by machine was considered in the early stages of computer vision and is now again revived after nearly a decade. Many literatures are available in this area such as neural nets (Shalkoff, 1997), karhuneun'loeve expression (Bounelli, 1993), fuzzy relational moments (Karibasappa, 1999), (Todd et al, 1996), is density lines and auto-correlation method (Goudail, 1996;Karibasappa, 1998), etc. There are two distinct techniques applied to recognition of digital images (Bernd, 1997).…”

Section: Introductionmentioning

confidence: 99%

Face Recognition by Bunch Graph Method Using a Group Based Adaptive Tolerant Neural Network

D¹,

Girish²,

Karibasappa³

et al. 2011

BJMCS

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This paper presents a new method for feature extraction from the facial image by using bunch graph method. These extracted geometric features of the face are used subsequently for face recognition by utilizing the group based adaptive neural network. This method is suitable, when the facial images are rotation and translation invariant. Further the technique also free from size invariance of facial image and is capable of identifying the facial images correctly when corrupted with noise/ camouflage. The method shows a result of 97% correct result in facial database of 320 images of 10 classes. The technique can be extended easily to incorporate the rotational invariance of the images.

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Image matching with fuzzy moment descriptors

Cited by 19 publications

References 10 publications

Chaos and Stability in the Dynamic Behavior of the Multi-emotion Interactions

Chaos and Stability in the Dynamic Behavior of the Multi-emotion Interactions

Visual Perception based Motion Planning of Mobile Robot using Road Sign

Face Recognition by Bunch Graph Method Using a Group Based Adaptive Tolerant Neural Network

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