Characterization of Mammogram Using Ensemble Classification Technique for Detection of Breast Cancer

Breast cancer is one of the most common known cancers in women today. Just like any other form of cancer an early detection of cancer provides better chances of cure. However, it is an arduous task for the radiologists to detect cancer accurately. Thus computer aided diagnosis of the mammographic images is the most popular medium to aid the radiologists in accurately classifying benign and malignant mammographic lesions. In this thesis an efficient approach is presented to classify the mammographic lesion for the detection of breast cancer. In this approach the extracted feature coefficients are balanced using Gaussian distribution. This distribution balances the class unbalanced dataset providing for better classification. This scheme uses Logit Boost classification technique. Logit Boost uses least squared regression cost function on the additive model of Adaboost. The standard MIAS database was used to obtain the mammographic lesions. With a classification accuracy rate of 99.1% and a performance index value of AUC = 0.98 in receiver operating characteristic (ROC) curve the results are pretty much optimal. These results are very promising when compared with existing methods

STEFANN: Scene Text Editor using Font Adaptive Neural Network

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.Comment: Accepted in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 202

On The Generalized Divided Differences

Let V ⊂ℜ be a finite set and f :ℜ→ℜ. f(V) is divided the difference of f at the points of V. The m-th order Peano derivative of f({x}∪V)with respect to x is called generalized divided difference and is denoted by fm (x, V). The properties of fm(x,V) are studied

Analysis of Trend in Groundwater-Quality Parameters: A Case Study

In the 21st century, groundwater has a pivotal role in ensuring water, food, and environmental securities worldwide. Systematic observation, protection and restoration are essential for sustainable management of water resources. Regular monitoring is key to investigate temporal changes in groundwater quality, and statistical trend tests define whether these changes are significant or not. This study focuses on investigating trend in seasonal groundwater quality in an alluvial coastal basin of West Bengal, India. The seasonal groundwater-quality data (pH, TH, TDS, Fe2+ and HCO3ˉ) of pre-monsoon and post-monsoon seasons were collected for 2011–2018 period and analyzed using three non-parametric statistical trend detection tests, namely: (i) Original Mann-Kendall (M-K) test, (ii) Modified Mann-Kendall (mM-K) test, and (iii) Spearman Rank Order Correlation (SROC) test. The trend magnitudes were estimated by using the Sen’s slope estimation test. Statistical analyses revealed that seasonal concentrations of all five groundwater-quality parameters have large spatial (block-wise) variation within the study area. The results of trend analyses indicated that seasonal TH and TDS concentrations mainly have significant decreasing trends (α = 5% or 1%), whereas seasonal HCO3ˉ and Fe2+ concentrations mostly show significant increasing trends (α = 5% or 1%) in different blocks. However, seasonal pH concentrations exhibited no trend. The mM-K test was found to be over-sensitive in finding trends than M-K and SROC tests. The SROC test was found to be less sensitive in detecting trends than M-K and mM-K tests. Trend magnitudes of seasonal pH, TH, TDS, HCO3ˉ and Fe2+ concentrations varied from –0.03/year to 0.23/year, –57.44 mg/L/year to 25.88 mg/L/year, –172.98 mg/L/year to 92.58 mg/L/year, –15.81 mg/L/year to 27.88 mg/L/year, and –0.05 mg/L/year to 0.61 mg/L/year, respectively. Continuous and proper groundwater-quality monitoring is critically required in all aquifer systems. The outcomes of this study will aid policy-makers in appropriately monitoring and managing groundwater quality

A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing

Air-writing refers to virtually writing linguistic characters through hand gestures in three-dimensional space with six degrees of freedom. This paper proposes a generic video camera-aided convolutional neural network (CNN) based air-writing framework. Gestures are performed using a marker of fixed color in front of a generic video camera, followed by color-based segmentation to identify the marker and track the trajectory of the marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies significantly on the illumination condition due to color-based segmentation. In a less fluctuating illumination condition, the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework has achieved 97.7%, 95.4% and 93.7% recognition rates in person independent evaluations on English, Bengali and Devanagari numerals, respectively.Comment: Accepted in The International Conference on Frontiers of Handwriting Recognition (ICFHR) 201