Palmprint for Individual’s Personality Behavior Analysis

2020 ◽  
Author(s):  
Shitala Prasad ◽  
Tingting Chai
Keyword(s):  
Behavior Analysis ◽  
Region Of Interest ◽  
Main Concern ◽  
Low Level ◽  
Average Accuracy ◽  
Personality Prediction ◽  
To Come ◽  
Hand Image ◽  
Head Line ◽  
Principal Lines

Abstract Palmprint is an important key player in biometric family and also informs some extra basic personality details of an individual. In this paper, we utilize these extra information and designed an automated mobile vision (MV) system to extract principal lines from human palm and analyze them for behavioral significances. Hence, the main concern of this paper is to come up with a simple yet powerful low-level MV solution to extract the complex challenging features from palmprint. In the proposed system, the computational tasks are offloaded to a dedicated palmistry server and efficiently minimizes the energy consumption of mobile device after performing some preliminary computational low-level tasks. The implementation is divided into four major phases: (i) hand-image acquisition and pre-processing, (ii) region-of-interest extraction from the palm images, (iii) post-processing to extract principal lines and (iv) features computation for behavior analysis. The basic palmistry uses line lengths, angles, curves and branches to identify a person’s behavior. The exhaustive experiments show that the proposed system achieves an average accuracy of 96%, 92% and 84% for heart, life and head line detection and personality prediction, respectively. Finally, mapping the extracted results with the original palmprint is augmented back to the use for better visualization.

TEXT LOCATION IN SCENE IMAGES USING VISUAL ATTENTION MODEL

2012 ◽  
Vol 26 (04) ◽  
pp. 1255008 ◽  
Author(s):  
QIAO-YU SUN ◽  
YUE LU
Keyword(s):  
Traditional Approach ◽  
Region Of Interest ◽  
Saliency Map ◽  
Image Features ◽  
Attention Model ◽  
Low Level ◽  
Edge Direction ◽  
Text Feature ◽  
Nature Scene ◽  
Direction Information

Locating text region from an image of nature scene is significantly helpful for better understanding the semantic meaning of the image, which plays an important role in many applications such as image retrieval, image categorization, social media processing, etc. Traditional approach relies on the low level image features to progressively locate the candidate text regions. However, these approaches often suffer for the cases of the clutter background since the adopted low level image features are fairly simple which may not reliably distinguish text region from the clutter background. Motivated by the recent popular research on attention model, salience detection is revisited in this paper. Based on the case of text detection on nature scene image, saliency map is further analyzed and is adjusted accordingly. Using the adjusted saliency map, the candidate text regions detected by the common low level features are further verified. Moreover, efficient low level text feature, Histogram of Edge-direction (HOE), is adopted in this paper, which statistically describes the edge direction information of the region of interest on the image. Encouraging experimental results have been obtained on the nature scene images with the text of various languages.

scholarly journals Melanoma Detection in Dermoscopic Images using Color Features

2019 ◽  
Vol 12 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Sameena Pathan ◽  
Vatsal Aggarwal ◽  
K. Gopalakrishna Prabhu ◽  
P. C. Siddalingaswamy
Keyword(s):  
Region Of Interest ◽  
Ensemble Classifiers ◽  
Dynamic Selection ◽  
Melanoma Detection ◽  
Color Features ◽  
Melanocytic Lesions ◽  
Average Accuracy ◽  
Malignant Lesions

Color is considered to be a major characteristic feature that is used for distinguishing benign and malignant melanocytic lesions. Most of malignant melanomas are characterized by the presence of six suspicious colors inspired from the ABCD dermoscopic rule. The presence of these suspicious colors histopathologically indicates the presence of melanin in the deeper layers of the epidermis and dermis. The objective of the proposed work is to evaluate the role of color features, a set of fifteen color features have been extracted from the region of interest to determine the role of color in malignancy detection. Further, a set of ensemble classifiers with dynamic selection techniques are used for classification of the extracted features, yielding an average accuracy of 87.5% for classifying benign and malignant lesions.

scholarly journals Analysis of Brain MRI Images Using Improved CornerNet Approach

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1856
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Momina Masood ◽  
Awais Mehmood ◽  
Rabbia Mahum ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Region Of Interest ◽  
Abnormal Growth ◽  
Novel Approach ◽  
Human Blood Cells ◽  
Average Accuracy ◽  
The One ◽  
Treatment Procedures

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

scholarly journals Detection and Classification of Ring, Rust and Yellow Sugarcane Leaf Diseases

2019 ◽  
Vol 8 (6) ◽  
pp. 3310-3315
Keyword(s):  
Social Life ◽  
Color Image ◽  
Region Of Interest ◽  
Support Vector ◽  
Svm Classifier ◽  
Morphological Operations ◽  
Color Processing ◽  
Grey Scale ◽  
Average Accuracy ◽  
Crop Disease

Agriculture is an important sector in Economic and Social life. Crop disease detection is an emerging field in India. We can minimize the diseases infection on sugarcane leaf by detecting and grading the leaf disease in early stages. In this paper, we are detecting and recognize Sugar cane leaf diseases by using grey scale and color image processing and analyze the efficacy by comparing both. In grey scale processing, we presented Gradient Magnitude, Otsu method, Morphological Operations and Normalization to extract the Region of interest (ROI) i.e., disease part. In color processing initially converted RGB to L*a*b format, later K-means clustering and edge detection operations are applied on L*a*b image format. The features of Grey scale & color processed image are extracted and feed to Support Vector Machine (SVM) classifier which classifies ring, rust & yellow spot sugarcane leaf diseases. The Sugarcane leaf diseases are classified successfully with an average accuracy of 84% & 92% for grey scale & color features respectively.

scholarly journals Development of Hand Gesture Based Electronic Key Using Microsoft Kinect

2018 ◽  
Vol 218 ◽  
pp. 02014
Author(s):  
Arief Ramadhani ◽  
Achmad Rizal ◽  
Erwin Susanto
Keyword(s):  
Computer Vision ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Hand Gesture ◽  
Depth Sensor ◽  
Average Accuracy ◽  
Recognition Result ◽  
Hand Image

Computer vision is one of the fields of research that can be applied in a various subject. One application of computer vision is the hand gesture recognition system. The hand gesture is one of the ways to interact with computers or machines. In this study, hand gesture recognition was used as a password for electronic key systems. The hand gesture recognition in this study utilized the depth sensor in Microsoft Kinect Xbox 360. Depth sensor captured the hand image and segmented using a threshold. By scanning each pixel, we detected the thumb and the number of other fingers that open. The hand gesture recognition result was used as a password to unlock the electronic key. This system could recognize nine types of hand gesture represent number 1, 2, 3, 4, 5, 6, 7, 8, and 9. The average accuracy of the hand gesture recognition system was 97.78% for one single hand sign and 86.5% as password of three hand signs.

Teaching About Teaching - A Strategy

1978 ◽  
Vol 5 (1&2) ◽  
pp. 81-90
Author(s):  
Ruby King
Keyword(s):  
Teacher Education ◽  
Student Teachers ◽  
Student Teacher ◽  
Main Concern ◽  
Educative Process ◽  
To Come ◽  
Supporting Materials ◽  
Actual Teaching

Central to the educative process is the transaction which takes place between the learner and the teacher. The main concern, therefore, of teacher education programmes is to improve the quality of future transactions by help­ing teachers (in-service and in-training) to understand the learner, the dynamics of learning, and their own roles in the transaction. The case study presented in this article is based on the premise that student-teachers will continue to grope in the dark until they have developed the necessary understandings and insights. It is imperative that our student-teachers receive such help as will enable them to reach the stage where teaching becomes a deliberate meaningful act. This breakthrough can be quickly effected when the student-teacher is made to come to grips with the realities of life in the classroom directly through actual teaching, and indirectly through appropriate room directly through actual teaching, and indirectly through appropriate supporting materials in the local idiom, which reflect circumstances and experiences with which he is familar.

General discussion

1988 ◽  
Vol 324 (1579) ◽  
pp. 467-469
Keyword(s):  
Remotely Sensed ◽  
Commodity Markets ◽  
The State ◽  
Main Concern ◽  
Remotely Sensed Data ◽  
Sufficient Knowledge ◽  
The World ◽  
To Come ◽  
The Military

Sir Hermann Bondi, K.C.B., F.R.S. (Churchill College, Cambridge, U. K. ). (i) I see that our main concern is to stimulate the potential civil users to come forward and let us know their requirements with some precision. Because it has become clear that there are no general answers to all questions, our work needs to be targetted. The military user can be more definite and say that he wants to see where specific targets are. Can one be equally specific about particular civil needs? (ii) Nor do I believe that money need be an obstacle. In the commodity markets an interpretation of remotely sensed data ahead of other knowledge should give a tremendous advantage to somebody buying or selling futures in, say, coffee. For, with sufficient knowledge and expertise, the data should reveal the state of the crop in all parts of the world. How can these sources of funds be tapped? (iii) What other civil uses might involve very large sums of money?

scholarly journals Vertical Variations of Boundary Layer Potential Buoyancy in Tornadic and Nontornadic Near-Storm Environments

2012 ◽  
Vol 27 (6) ◽  
pp. 1489-1506 ◽  
Author(s):  
Chauncy J. Schultz ◽  
Mark A. Askelson
Keyword(s):  
Boundary Layer ◽  
Radar Reflectivity ◽  
Layer Potential ◽  
Low Level ◽  
Vertical Change ◽  
Vertical Variations ◽  
To Come

Abstract Despite great strides in understanding the tornadic near-storm environment (NSE), at times it remains difficult to determine why some storms produce significant tornadoes, while others produce none, given similar pretornadic radar reflectivity and velocity signatures. Previous studies have shown that this is likely related to the potential buoyancy (θep) of the rear-flank downdraft (RFD) air. Unfortunately, to date there are few ways to operationally anticipate possible RFD thermodynamic character. Based upon previous research indicating that capping inversions may restrict much of the low-level RFD air to come from within the boundary layer, this study considers the relation of Δθep (vertical change in θep within the boundary layer below the cap) to tornadogenesis potential. This is because when a cap exists above a boundary layer and the descent of lower-θep air from aloft to the surface is potentially limited, then minimal Δθep may indicate more RFD air that has greater potential buoyancy. The Rapid Update Cycle (RUC) soundings used in this study and several observed soundings taken in the vicinity of violent tornadoes suggest that boundary layer Δθep shows promise as an additional means of discriminating between tornadic and nontornadic NSEs.

scholarly journals P43 POEMS neuropathy in the rheumatology clinic

Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Lisa Waters ◽  
Rosalind Benson ◽  
Madhu Mahindrakar ◽  
Robert Moots ◽  
Rikki Abernethy
Keyword(s):  
Lumbar Puncture ◽  
Bladder Wall ◽  
Musculoskeletal Symptoms ◽  
Main Concern ◽  
Cell Transplant ◽  
Rheumatology Clinic ◽  
Skin Changes ◽  
Low Level ◽  
Inflammatory Neuropathy ◽  
Bony Metastases

Abstract Background POEMS (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, skin changes) is a rare, disabling, auto-inflammatory condition that may present to multiple specialties including rheumatology. We describe a case fulfilling all diagnostic criteria. Methods Please refer to the results section. Results A 52 year old lady of Venezuelan origin was referred to the rheumatology clinic with myalgia and arthralgia. She had a recent diagnosis of hypothyroidism. Her main concern was of skin hyperpigmentation, hypertrichosis and a reticular rash. She had been diagnosed with undifferentiated connective tissue disease (CTD) in Panama and had received a good symptomatic response to IM depomedrone. Initial examination findings revealed generalised skin pigmentation, hyperaemia and limited weakness of both shoulders. There were no other features of CTD. Initial bloods revealed normal FBC, UE, LFTS, CRP 18mg/l and ESR 37mm/hr. Serum electrophoresis, immunoglobulins, CK, cortisol, calcium, TSH, ANCA and urinalysis were normal. ANA was normal but had previously been positive in Panama. MRI of spine showed multiple abnormalities consistent with bony metastases. CT of chest, abdomen and pelvis (CAP) confirmed changes consistent with widespread sclerotic bony metastases with bilateral axillary lymphadenopathy, splenomegaly and diffuse bladder wall thickening. Lymph node biopsy confirmed Castleman-like changes, but this was not pathognomonic. Thyroid biopsy showed reactive changes. A bone biopsy of a sacral lesion revealed haemangioma. A PET scan highlighted sclerotic lesions and splenomegaly only. Mammogram and cystoscopy were normal. She then presented via ophthalmology to the acute medical unit at a different hospital with visual disturbance and confirmed papilloedema. CT head was normal. Lumbar puncture confirmed high CSF pressure and high protein levels of 1.75g/l. MRI of brain with enhancement showed leptomeningeal enhancement felt to be post-lumbar puncture change. Repeat CT CAP showed mild ascites and splenomegaly. Extensive infectious disease tests were all negative including HIV and TB Quantiferon. A review of medical literature and input from radiologists raised the diagnosis of POEMS. Immunofixation was requested and this confirmed a low level IGA lamba monoclonal band. Vascular endothelial growth factor was significantly elevated at 4800. Bone marrow biopsy confirmed low level lambda restricted plasma cell infiltration consistent with POEMS. She is now being treated with Lenalidomide and dexamethasone under the care of haematology and is likely to require an autologous stem cell transplant in the future. Conclusion The challenge of diagnosing POEMS is well recognised. Few patients meet the full criteria for diagnosis. The heterogeneity of its clinical presentation means that patients may present to several specialities with multiple complaints. The combination of musculoskeletal symptoms, skin changes and inflammatory neuropathy may give rise to a rheumatology opinion and therefore it is important rheumatologists have awareness of the condition. We recommend that POEMS should be considered in patients presenting with an inflammatory neuropathy. Disclosures L. Waters None. R. Benson None. M. Mahindrakar None. R. Moots None. R. Abernethy None.

scholarly journals A SVM Model for Candidate Y-chromosome Gene Discovery in Prostate Cancer

10.29007/3nzw ◽  
2019 ◽  
Author(s):  
Wageesha Rasanjana ◽  
Sandun Rajapaksa ◽  
Indika Perera ◽  
Dulani Meedeniya
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Y Chromosome ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Support Vector ◽  
Body Tissues ◽  
Average Accuracy ◽  
To Come ◽  
Significant Expression

Prostate cancer is widely known to be one of the most common cancers among men around the world. Due to its high heterogeneity, many of the studies carried out to identify the molecular level causes for cancer have only been partially successful. Among the techniques used in cancer studies, gene expression profiling is seen to be one of the most popular techniques due to its high usage. Gene expression profiles reveal information about the functionality of genes in different body tissues at different conditions. In order to identify cancer-decisive genes, differential gene expression analysis is carried out using statistical and machine learning methodologies. It helps to extract information about genes that have significant expression differences between healthy tissues and cancerous tissues. In this paper, we discuss a comprehensive supervised classification approach using Support Vector Machine (SVM) models to investigate differentially expressed Y-chromosome genes in prostate cancer. 8 SVM models, which are tuned to have 98.3% average accuracy have been used for the analysis. We were able to capture genes like CD99 (MIC2), ASMTL, DDX3Y and TXLNGY to come out as the best candidates. Some of our results support existing findings while introducing novel findings to be possible prostate cancer candidates.

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