Competition Name: The Nature Conservancy Fisheries Monitoring
Pre-processing: Median Filtering, Histogram Equalization
Segmentation: K-Means Clustering
Feature Extraction: Oriented FAST and Rotated BRIEF (ORB)
The dataset compiled by The Nature Conservancy in partnership with Satlink, Archipelago Marine Research, the Pacific Community, the Solomon Islands Ministry of Fisheries and Marine Resources, the Australia Fisheries Management Authority, and the governments of New Caledonia and Palau.
The Train and Test dataset consists of images having only one fish category out of eight different categories mentioned as follows:
1. Albacore tuna
2. Bigeye tuna
3. Yellowfin tuna
7. Other (i.e. fish present but not the defined categories)
8. No Fish (i.e. no fish is in the image)
Input: Training Set and Test Set of Images
Output: Classification Score
1. Read the input RGB image
2. Perform median filtering for noise removal
3. Perform histogram equalization for image enhancement
4. Perform the K-Means clustering for segmentation of pre-processed image
5. Applying Oriented FAST and Rotated BRIEF (ORB) method for feature extraction
6. Apply K-Nearest Neighbour (KNN) classifier for recognition
The algorithm initiates into reading the input RGB images from the training set and store them into array. A 5X5 window-based 2D convolution operation is applied on the input images for median filtering. In this process, the neighboring pixels are ordered according to their intensity values and the median value becomes the resulting output for the central pixel of the defined window. Median filters can handle efficiently with noise, in particular impulse noise in which some individual pixels have extreme values while preserving the contrast within the images.
Contrast is a significant image element that can be defined as a ratio between the highest and the lowest pixel intensities of an image. As the images usually suffer from poor image quality, degradation in contrast and happening of shading and artefacts, the lack in centering pixel intensity, poor lightening, specimen spotting are important factors that affect the images which leads to enhance the contrast. In our methodology, histogram equalization is applied for image enhancement. Histogram of images provides a pixel-wise intensity distribution and description of the image appearance globally. The equalization process mapping histogram distribution to a wider and more uniform distribution of intensity values, as a result the intensity values are spread over the whole range of intensity distribution of the images.
The pre-processed images are segmented based on K-Means clustering approach for extraction of the region of interest. K-Means is an unsupervised learning process which assigns and groups the image pixels into a predefined number of clusters based on the calculated similarity for each pixel associate with the nearest center pixel. When all image pixels have been assigned, this iterative process continues with the new calculated center pixels. The process initiates the center pixels corresponding to the clusters randomly and keeps reassigning until criterion is met:
The K-Means objective function:
where is a chosen distance measure between a data point and the cluster centre , is an indicator of the distance of the n data points from their respective cluster centres.
After applying segmentation process, the Oriented FAST and Rotated BRIEF (ORB) method is used to extract significant features. This method combines FAST（Feature from Accelerated Segment test) keypoint detector and BRIEF (Binary Robust Independent Elementary Features) descriptor approach. This method initiates with identification of FAST corner keypoints within the image region based on comparison of the intensity threshold value between the center pixel and those in neighborhood around that pixel. Harris corner extracts the first target N number of FAST keypoints in order to employ them for further processing. A scale pyramid process is incorporated in order to extract N number of FAST keypoints at each level in the pyramid. The intensity centroid value is also measured for each corner orientation for achieving the direction of corresponding keypoints. BRIEF extracts descriptors around selected key feature points through binary coding.
Where, p(x) is the pixel intensity at that point x in image region
p(y) is the pixel intensity at that point y in image region
a set of points can uniquely identify one binary detection τ
K-nearest Neighbor algorithm incorporates the extracted key feature points in order to classify the test image data based on the trained information.
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Pl find Below attached Syllabus for CQF Exam for your ready references :
CQF Syllabus mentioned below :
Random Behaviour of Assets
Different types of financial analysis
• Examining time-series data to model returns
• Are prices random?
• The need for probabilistic models
• The Wiener process, a mathematical model of randomness
• The lognormal random walk—The most important model for
equities, currencies, commodities and indices
Transition Density Functions
• A Brownian motion
• A trinomial random walk
• Transition density functions
• Our first differential equation
• Similarity solutions
Stochastic Calculus and Itˆo’s Lemma
Stochastic calculus is very important in the mathematical mod-
eling of financial processes. This is because of the underlying
(assumed) random nature of financial markets.
Simulating and Manipulating Stochastic Differential Equations
Using Itˆo’s lemma to manipulate stochastic differential equa-tions
• Continuous-time stochastic differential equations as discrete-
• Simple ways of generating random numbers in Excel
• Correlated random walks
a simple model for an asset price random walk
• delta hedging
• no arbitrage
• the basics of the binomial method for valuing options
• risk neutrality
Stochastic Calculus Toolbox, Part II Martingales
we talk about martingales:
I What is a martingale?
I Martingales and Itˆo calculus
I Martingale unmasked: how do I know if my stochastic process
is a martingale?
I Exponential martingales, Girsanov and change of measure
Euler discretization of SDE
Consider Geometric Brownian Motion (GBM) as a model for the dynamics
of a financial asset: dS = μSdt + σSdW (1)
This asset could be the price of a share of company. Using Euler dis-
cretization compute one potential future path for the evolution of the as-
set for a period of one week (Mon thru Fri). Consider a flat interest rate
of 5% pa and a volatility of 30 % pa. The initial price (as observed to-
day) is S0=100 USD on Monday EOD. Use a pre-computed vector of ran-
dom numbers (samples from a standard normal distribution) composed of
1 = +0.4423,2 = −0.1170,3 = +0.0291,4 = +0.6872.
2 Monte Carlo Simulation
We would like to price a six-month European Call option on Vodafone equity
(VOD). The current equity price of Vodafone is 100 USD, with a volatility 1
of 20 percent and a strike of 100 USD. We assume that the stock pays no
dividends. The current interest risk-free rate is 5 percent pa. Assume that
we are given the following pre-computed five trajectories, in the table below,
based on using GBM. Each line contains the values of the assets and the
random samples used. What is the price of the premium of this option using
Financial Regulation and Basel III
“Currently, every European bank must observe approximately 40,000 legally binding
requirements of the European Union. In the field of banking
supervision, four thousand and one different rules have been set
down on 34,019 pages … Today it is almost impossible to find any
banking supervisor or bank practitioner who is able to explain exactly
the supervisory rules and their consequences. The scope and
complexity of the rules are just too great.”
Value at Risk and Expected Shortfall
risk management is the identification, assessment, and prioritization of risks
followed by coordinated and economical application of resources to minimize,
monitor, and control the probability and/or impact of unfortunate events or to
maximize the realization of opportunities.
Fundamentals of Optimization and Application to Portfolio Selection
how to formulate an optimization problem;
I elementary rules and tips.
II. Unconstrained Optimization Problems
I how to use calculus to solve unconstrained optimization problems;
I application to mean-variance optimization;
I application to linear regression.
III. Optimization problems with equality constraints
I the method of Lagrange;
I application to portfolio selection;
I the minimum variance portfolio;
I the tangency portfolio.
IV. The Black-Litterman model.
V. A short note on optimization problems with inequality constraints: the
Asset Returns: Key, Empirical Stylized Facts
Know about the most important empirical properties of asset returns,
• Know that changes in volatility explain many empirical effects,
• Have seen several examples of time series,
• Have seen several examples of autocorrelations.
Volatility Models: the ARCH framework
Know there are many ways to define volatility,
• Know why ARCH models are often estimated from daily price series,
• Appreciate that estimating ARCH models and volatilities is straightforward,
• Have seen simple equations for predicting volatility,
• Realize that stock index volatility generally moves in the opposite direction to
Liquidity Asset Liability Management
Bear Stearns & Co., the fifth-largest U.S. investment bank as 2008 began,
burned through nearly all of its $18 billion in cash reserves during the week
of March 10, 2008.
• Bear survived to the dose of business on Friday, March 14 only because of
that morning’s groundbreaking announcement: the Federal Reserve Bank
of New York, using JP Morgan Chase & Co. as a conduit, would provide
Bear with secured financing for a period of up to 28 days.
• Despite this unprecedented provision of liquidity support from the Federal
Reserve System to an investment bank, it was insufficient to reverse
Bear’s condition, and on Friday evening, Bear CEO Alan Schwartz learned
Bear’s access to the N.Y. Fed’s new lending facility would last only one day.
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EXAM SCHEDULE FOR JANUARY 2019 CQF PROGRAM is ATTACHED BELOW :
EXAM RELEASED SUBMISSION DEADLINE
#EXAM 1 14 March 2019 28th March 2019
#EXAM 2 15th April 2019 29 April 2019
#EXAM3 03 June 2019 17 June 2019
#FINAL PROJECT 20 May 2019 15 JULY 2019
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